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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Haozhi Wang; Qing Wang; Luyong Chen; Guanyang Fu; +3 Authors

    Intelligent electromagnetic signature recognition is one of the key technologies in Internet-of-Things (IoT) device connection, which can improve system security and speed up the authentication process. In practical scenarios, as the number of IoT devices increases, electromagnetic features such as fingerprint and modulation signals also increase substantially. However, since intelligent recognition technology, such as Automatic Modulation Classification (AMC), requires a large amount of labeled data to train the neural network classifier, it is challenging to collect so much labeled data. To address the performance degradation challenges with small training data, we propose an efficient semi-supervised electromagnetic recognition framework to break the performance gap with the fully supervised learning scheme. This framework can fully use the unlabeled electromagnetic data collected during the authentication process for self-training to improve the classifier’s performance. According to the idea of consistency regularization, we design a signal augmentation method and propose an ensemble pseudo-label design algorithm to improve confidence. Moreover, we perform a convex combination of electromagnetic features to smooth the model decision boundary while generalizing to unknown data distribution regions. Experimental results on the modulated data demonstrate the performance superiority of the proposed algorithm, i.e., use less than 5% of data with no more than 10% performance drop. IEEE

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.1109/jiot.2...
    Article . 2024 . Peer-reviewed
    License: IEEE Copyright
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      https://doi.org/10.1109/jiot.2...
      Article . 2024 . Peer-reviewed
      License: IEEE Copyright
      Data sources: Crossref
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ting Jin; Lei Li;

    ABSTRACT: Pork price fluctuations are closely related to the national economy and people’s livelihoods in China. Based on the monthly pork price fluctuations in China from January 2011 to August 2022, this study uses ARCH family models to assess the characteristics and laws of these fluctuations in China. The pork price fluctuations show obvious clustering, with external shock information from the previous month affecting the pork price in the following period; the pork market price is characterized by risk compensation, with the high risk of pork supply driving the pork price up. In addition, the pork price fluctuations are characterized by asymmetry, with a greater impact of good than of bad news on the pork price. Due to the pork industry’ low entry threshold and the existence of sunk costs, positive information on the pork market has a stronger impact on price fluctuations than negative information. To guide pork supply, we recommend improving monitoring and early-warning mechanisms in the pork market to identify the pork price volatility threshold and measure the price volatility. In addition, price index insurance products should be constantly strengthened, with different types of insurance products being offered to meeting the insurance demand of various sectors in the pig meat supply chain.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Ciência Ruralarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Ciência Rural
    Article . 2024 . Peer-reviewed
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    Article . 2023
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Ciência Ruralarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Ciência Rural
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Chen FeiZhang; Xiao HongXie; Yong Hong Jia; Qing HaoWang; +6 Authors

    ABSTRACT: Rhododendron fortune belongs to a scented Rhododendron species native to China, which produces fragrant flowers of great ornamental and environmental values for landscaping or indoor beautification. However, the scents in Rhododendron fortuneihave not yet been investigated, let alone the mechanism of the formation of these fragrance in the flowers. In this study, we measured the scents in terms of its volatile components and contents (VOC) in Rhododendron fortuneiat four different flowering stages and in different tissues of the plant by headspace solid-phase micro-extraction combined (HS-SPME) with gas chromatography-mass spectrometry (GC-MS). Then the characteristic aromatic values, which reflects the degree of scent perception by human, of each VOC in the plant was calculated according to its respective aromatic thresholds. Results showed that three main VOCs measured from highest to lowest are methyl benzoates, terpenes and fatty acid derivatives. Their content increased after the flower bud opening and reached the highest at half to full blossom. In a flower most VOC contents were measured in petals and only trace amount in other tissues such as stamen, pistil. A small amount of VOCs was determined in leaves as well.All aromatic values were almost corresponded to the contents of three main VOCs, indicating that the flower fragrance arises truly from these VOC components. S-adenosyl-L-methionine: benzoic acid carboxyl methyl transferase (BAMT) catalyzes the final step to form methyl benzoates. To understand the mechanism of the formation of this main type fragrance and its regulation, we firstly isolate a gene of RfBAMT from petal of Rhododendron fortuneiby using homologous cloning and RACE technology. The full length of its cDNA was 1383 bp,with an open reading frame of 1104 bp, encoding a total of 368 amino acids. The phylogenetic tree analysis showed that RfBAMT was the closest to the BSMT of Camellia japonica, belonging to methyltransferases family. Then we measured the expression level of RfBAMT again at four flower developmental stages and in different flower tissues and leaves. The results showed that the expression level of this gene was highly positively correlated with the emitted content of methyl benzoates in the flowering, implying that RfBAMT plays a pivotal role in the formation and regulation of methyl benzoates in Rhododendron fortune.Thisresearchshowed that the RfBAMT was cloned and identified in our study and its expression level was highly positively correlated with the emitted content of methyl benzoates in the flowers and leaves, which indicated this gene may play an important role on regulation of methyl benzoate synthesis in Rhododendron fortunei. RESUMO: Rhododendron fortunei pertence a uma espécie de rododendro perfumada nativa da China, que produz flores perfumadas de grande valor ornamental e ambiental para paisagismo ou embelezamento de interiores. No entanto, os aromas em Rhododendron fortunei ainda não foram investigados, muito menos o mecanismo de formação dessas fragrâncias nas flores. Neste estudo, medimos os aromas em termos de seus componentes e conteúdos voláteis (VOC) em Rhododendron fortunei em quatro diferentes estágios de floração e em diferentes tecidos da planta por microextração em fase sólida headspace combinada com cromatografia gasosa-espectrometria de massa. Em seguida, foram calculados os valores aromáticos característicos, que refletem o grau de percepção olfativa pelo ser humano, de cada VOC na planta de acordo com seus respectivos limiares aromáticos. Os resultados mostraram que três COVs principais medidos do mais alto ao mais baixo são benzoatos de metila, terpenos e derivados de ácidos graxos. Seu conteúdo aumentou após a abertura do botão floral e atingiu o máximo na metade da floração total. Em uma flor, a maioria dos teores de COV foram medidos em pétalas e apenas traços em outros tecidos, como estame, pistilo. Uma pequena quantidade de COVs foi determinada nas folhas também. Todos os valores aromáticos foram quase correspondentes aos teores de três COVs principais, indicando que a fragrância da flor surge verdadeiramente desses componentes de COV. Para entender o mecanismo de formação deste tipo principal de fragrância e sua regulação, primeiramente isolamos um gene de RfBAMT da pétala de Rhododendron fortunei usando clonagem homóloga e tecnologia RACE. O comprimento total de seu cDNA era de 1383 bp, com um quadro de leitura aberto de 1104 bp, codificando um total de 368 aminoácidos. A análise da árvore filogenética mostrou que RfBAMT foi o mais próximo do BSMT de Camellia japonica, pertencente à família das metiltransferases. Em seguida, medimos o nível de expressão de RfBAMT novamente em quatro estágios de desenvolvimento da flor e em diferentes tecidos e folhas de flores. Os resultados mostraram que o nível de expressão deste gene foi altamente correlacionado positivamente com o conteúdo emitido de benzoatos de metila na floração, implicando que RfBAMT desempenha um papel fundamental na formação e regulação de benzoatos de metila em Rhododendron fortune foi clonado e identificado em nosso estudo e seu nível de expressão foi altamente correlacionado positivamente com o conteúdo emitido de benzoatos de metila nas flores e folhas, o que indicou que este gene pode desempenhar um papel importante na regulação da síntese de benzoato de metila em Rhododendron fortunei.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Scientific Electroni...arrow_drop_down
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    Ciência Rural
    Article . 2024 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Scientific Electroni...arrow_drop_down
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      Ciência Rural
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lan Zhang; Yang Wang; Linzi Yang; Jianfeng Chen; +3 Authors

    Hyperspectral image (HSI) classification has become a popular research topic in recent years, and transformer-based networks have demonstrated superior performance by analyzing global semantic features. However, using transformers for pixel-level HSI classification has two limitations: ineffective capture of spatial-spectral correlations and inadequate exploitation of local features. To address these challenges, we propose a dual-dimension self-attention (D2SA) mechanism that fully exploits HIS's high spectral-spatial correlation by using two separate branches to model the global dependence of features from the spectral and spatial dimensions. Additionally, we develop a multilayer residual convolution module that extracts local features and introduces shallow-deep feature interactions to obtain more discriminative representations. Based on these components, we propose a dual-dimension spectral-spatial bottleneck transformer (D2S2BoT) framework for HSI classification that simultaneously models the local interactions and global dependencies of HSI pixels to achieve high-precision classification. By virtue of the D2SA mechanism, the introduced D2S2BOT framework can produce competitive classification results with a limited number of training samples on three well-known datasets, which we hope will provide a strong baseline for future research on transformers in the field of HSI.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ IEEE Journal of Sele...arrow_drop_down
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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
    Article . 2024 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ IEEE Journal of Sele...arrow_drop_down
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Qian Cheng; Xin Li; Taoyang Wang; Boyang Jiang; +3 Authors

    Remote sensing sensor platforms are typically located at a significant distance from the ground, ranging from several hundred meters to hundreds of kilometers. This means that, compared to natural images, remote sensing images (RSI) have larger coverage areas and more complex information. The larger size and data volume of RSI presents challenges for computer vision matching algorithms (MAs), making it difficult to apply them directly to RSI matching. Moreover, a matching framework for multisource RSIs capable of large-scale processing by integrating multiple MAs with the entire RSI as input is presently lacking. This study proposes a tie points (TPs) matching framework of multisource remote sensing images based on the geometric and radiation characteristics of RSI. First, RSI is divided into different grids and undergoes local geometry correction. Next, matching between slice images is performed by MAs. Finally, TPs are generated by mapping matched points in multiple slice images to the whole RSI using a geometric processing model. Six representative MAs including artificial feature MAs and deep learning algorithms are integrated into the framework to match TPs from different RSI. Results demonstrate the extraction of TPs for multisource RSI, validating the framework's efficacy. In addition, a large-scale TPs matching test for deep learning MA is performed by using 13 synthetic aperture radar images (10-m resolution) with TPs root mean square error of 0.368 pixels, further confirming the framework's reliability.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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    Authors: Wensheng Fan; Fan Liu; Jingzhi Li;

    Pansharpening is a fundamental and crucial image processing task for many remote sensing applications, which generates a high-resolution multispectral image by fusing a low-resolution multispectral image and a high-resolution panchromatic image. Recently, vision transformers have been introduced into the pansharpening task for utilizing global contextual information. However, long-range and local dependencies modeling and multiscale feature learning are all essential to the pansharpening task. Learning and exploiting these various information raises a big challenge and limits the performance and efficiency of existing pansharpening methods. To solve this issue, we propose a pansharpening network based on multiscale embedding and dual attention transformers (MDPNet). Specifically, a multiscale embedding block is proposed to embed multiscale information of the images into vectors. Thus, transformers only need to process a multispectral embedding sequence and a panchromatic embedding sequence to efficiently use multiscale information. Furthermore, an additive hybrid attention transformer is proposed to fuse the embedding sequences in an additive injection manner. Finally, a channel self-attention transformer is proposed to utilize channel correlations for high-quality detail generation. Experiments over QuickBird and WorldView-3 datasets demonstrate the proposed MDPNet outperforms state-of-the-art methods visually and quantitatively with low running time. Ablation studies further verify the effectiveness of the proposed multiscale embedding and transformers in pansharpening.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lele Li; Liejun Wang; Anyu Du; Yongming Li;

    In the field of remote sensing, change detection is a crucial study area. Deep learning has made significant strides in the study of remote sensing image change detection during the past few years. Deep learning techniques still have some drawbacks. The global context cannot be modeled by convolutional neural networks due to the receptive field's restrictions. When extracting visual characteristics, the neural network does not concentrate more on the change region, which results in poor distinction between change and no-change regions. To address these problems, we propose networks with large receptive fields (LRFs) and difference image enhancement. First, we design the LRF strategy. It employs a long kernel shape in one spatial dimension for obtaining a long range of relations. Keeping a narrow kernel size in the other spatial dimension can extract local context information while avoiding interference from irrelevant regions. To focus on the changing features, we design the image difference enhancement (IDE) method, which decreases the distance between invariant features and enlarges the distance between changing features. In addition, we design the cross-channel interaction (CNI) strategy, which models the relationship between feature map channels and extracts feature representations through local CNI. On the CDD, WHU-CD, and LEVIR-CD public datasets, we conducted comprehensive experiments. According to the experimental results, our proposed LRDE-Net performs better than other state-of-the-art change detection techniques, and the change regions are more precisely identified. It can better cope with seasonal changes, light intensity, and other pseudochange disturbances.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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    Authors: Xianxuan Long; Wei Zhuang; Min Xia; Kai Hu; +1 Authors

    With increasingly rapid development of convolutional neural networks, the field of remote sensing has experienced a significant revitalization. However, understanding and detecting surface changes, which necessitate the identification of high-resolution remote sensing images, remain substantial challenges in achieving precise change detection. Excited deep learning-based change detection techniques often exhibit limitations and lack the necessary precision to detect edge details or other nuanced information in remote sensing images. To address these limitations, we propose a unique semantic segmentation deep learning network, the self-adaptive Siamese network (SASiamNet), specifically devised for enhancing change detection in remote sensing images. The SASiamNet excels in real-time land cover segmentation, adeptly extracting local and global information from images via the backbone residual network. Furthermore, it incorporates a primary feature fusion module to extract and fuse the primary stage feature map, and a high-level information refinement module to refine the resultant feature map. This methodology effectively transmutes low-level semantic information into high-level semantic information, thereby improving the overall detection process. Aimed at empirically testing the effectiveness of the SASiamNet, we utilize two distinct datasets: the public dataset, LEVIR-CD, and a challenging dataset, CDD. The latter is composed of bitemporal images sourced from Google Earth, spanning various regions across China. The experiment results unequivocally demonstrate that our approach outperforms traditional methodologies as well as contemporary state-of-the-art change detection techniques, hence underscoring the efficacy of the SASiamNet in the context of remote sensing image change detection.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Yiying Zhang; Yue Huang; Chao Huang; Hailong Huang; +1 Authors

    International audience; Internet of Things (IoT) devices have been widely deployed to build smart cities. How to efficiently collect data from large-scale IoT devices is a valuable and challenging research topic. Benefiting from agility, flexibility, and deployability, an unmanned aerial vehicle (UAV) has great potential to be an aerial base station. However, given the limited battery capacity, the flight time of a UAV is limited. This article focuses on using multi-UAVs to execute long-distance data collection from large-scale IoT devices. We design a multi-UAVs-assisted large-scale IoT data collection system. The core facilities of this system are the data center and charging stations, which are equipped with a limited number of charging piles to provide charging services for UAVs. To ensure the efficient operation of the system, the problem of deployment and flight planning of UAVs is formulated as a joint optimization problem. To solve the problem, a population-based optimization algorithm with a three-layer structure, namely, EDDE-DPDE, is proposed. It includes two core components: 1) elite-driven differential evolution (EDDE) and 2) differential evolution with a dynamic population (DPDE), which are two variants of differential evolution. Thanks to ideas of reusing elite individuals and historical information, the proposed EDDE-DPDE shows an improvement of at least 11.11% compared with four powerful algorithms in terms of average travel time.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
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    https://doi.org/10.1109/jiot.2...
    Article . 2024 . Peer-reviewed
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    Authors: Hao Yi; Bo Liu; Bin Zhao; Enhai Liu;

    Due to the limitations of small targets in remote sensing images, such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bidirectional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top–down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground-truth box. Experimental results on NWPU VHR-10, RSOD, and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters, and FPS, and can prove that our modifications made to the original YOLOv8 model are effective.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Haozhi Wang; Qing Wang; Luyong Chen; Guanyang Fu; +3 Authors

    Intelligent electromagnetic signature recognition is one of the key technologies in Internet-of-Things (IoT) device connection, which can improve system security and speed up the authentication process. In practical scenarios, as the number of IoT devices increases, electromagnetic features such as fingerprint and modulation signals also increase substantially. However, since intelligent recognition technology, such as Automatic Modulation Classification (AMC), requires a large amount of labeled data to train the neural network classifier, it is challenging to collect so much labeled data. To address the performance degradation challenges with small training data, we propose an efficient semi-supervised electromagnetic recognition framework to break the performance gap with the fully supervised learning scheme. This framework can fully use the unlabeled electromagnetic data collected during the authentication process for self-training to improve the classifier’s performance. According to the idea of consistency regularization, we design a signal augmentation method and propose an ensemble pseudo-label design algorithm to improve confidence. Moreover, we perform a convex combination of electromagnetic features to smooth the model decision boundary while generalizing to unknown data distribution regions. Experimental results on the modulated data demonstrate the performance superiority of the proposed algorithm, i.e., use less than 5% of data with no more than 10% performance drop. IEEE

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.1109/jiot.2...
    Article . 2024 . Peer-reviewed
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      https://doi.org/10.1109/jiot.2...
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ting Jin; Lei Li;

    ABSTRACT: Pork price fluctuations are closely related to the national economy and people’s livelihoods in China. Based on the monthly pork price fluctuations in China from January 2011 to August 2022, this study uses ARCH family models to assess the characteristics and laws of these fluctuations in China. The pork price fluctuations show obvious clustering, with external shock information from the previous month affecting the pork price in the following period; the pork market price is characterized by risk compensation, with the high risk of pork supply driving the pork price up. In addition, the pork price fluctuations are characterized by asymmetry, with a greater impact of good than of bad news on the pork price. Due to the pork industry’ low entry threshold and the existence of sunk costs, positive information on the pork market has a stronger impact on price fluctuations than negative information. To guide pork supply, we recommend improving monitoring and early-warning mechanisms in the pork market to identify the pork price volatility threshold and measure the price volatility. In addition, price index insurance products should be constantly strengthened, with different types of insurance products being offered to meeting the insurance demand of various sectors in the pig meat supply chain.

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      Ciência Rural
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    Authors: Chen FeiZhang; Xiao HongXie; Yong Hong Jia; Qing HaoWang; +6 Authors

    ABSTRACT: Rhododendron fortune belongs to a scented Rhododendron species native to China, which produces fragrant flowers of great ornamental and environmental values for landscaping or indoor beautification. However, the scents in Rhododendron fortuneihave not yet been investigated, let alone the mechanism of the formation of these fragrance in the flowers. In this study, we measured the scents in terms of its volatile components and contents (VOC) in Rhododendron fortuneiat four different flowering stages and in different tissues of the plant by headspace solid-phase micro-extraction combined (HS-SPME) with gas chromatography-mass spectrometry (GC-MS). Then the characteristic aromatic values, which reflects the degree of scent perception by human, of each VOC in the plant was calculated according to its respective aromatic thresholds. Results showed that three main VOCs measured from highest to lowest are methyl benzoates, terpenes and fatty acid derivatives. Their content increased after the flower bud opening and reached the highest at half to full blossom. In a flower most VOC contents were measured in petals and only trace amount in other tissues such as stamen, pistil. A small amount of VOCs was determined in leaves as well.All aromatic values were almost corresponded to the contents of three main VOCs, indicating that the flower fragrance arises truly from these VOC components. S-adenosyl-L-methionine: benzoic acid carboxyl methyl transferase (BAMT) catalyzes the final step to form methyl benzoates. To understand the mechanism of the formation of this main type fragrance and its regulation, we firstly isolate a gene of RfBAMT from petal of Rhododendron fortuneiby using homologous cloning and RACE technology. The full length of its cDNA was 1383 bp,with an open reading frame of 1104 bp, encoding a total of 368 amino acids. The phylogenetic tree analysis showed that RfBAMT was the closest to the BSMT of Camellia japonica, belonging to methyltransferases family. Then we measured the expression level of RfBAMT again at four flower developmental stages and in different flower tissues and leaves. The results showed that the expression level of this gene was highly positively correlated with the emitted content of methyl benzoates in the flowering, implying that RfBAMT plays a pivotal role in the formation and regulation of methyl benzoates in Rhododendron fortune.Thisresearchshowed that the RfBAMT was cloned and identified in our study and its expression level was highly positively correlated with the emitted content of methyl benzoates in the flowers and leaves, which indicated this gene may play an important role on regulation of methyl benzoate synthesis in Rhododendron fortunei. RESUMO: Rhododendron fortunei pertence a uma espécie de rododendro perfumada nativa da China, que produz flores perfumadas de grande valor ornamental e ambiental para paisagismo ou embelezamento de interiores. No entanto, os aromas em Rhododendron fortunei ainda não foram investigados, muito menos o mecanismo de formação dessas fragrâncias nas flores. Neste estudo, medimos os aromas em termos de seus componentes e conteúdos voláteis (VOC) em Rhododendron fortunei em quatro diferentes estágios de floração e em diferentes tecidos da planta por microextração em fase sólida headspace combinada com cromatografia gasosa-espectrometria de massa. Em seguida, foram calculados os valores aromáticos característicos, que refletem o grau de percepção olfativa pelo ser humano, de cada VOC na planta de acordo com seus respectivos limiares aromáticos. Os resultados mostraram que três COVs principais medidos do mais alto ao mais baixo são benzoatos de metila, terpenos e derivados de ácidos graxos. Seu conteúdo aumentou após a abertura do botão floral e atingiu o máximo na metade da floração total. Em uma flor, a maioria dos teores de COV foram medidos em pétalas e apenas traços em outros tecidos, como estame, pistilo. Uma pequena quantidade de COVs foi determinada nas folhas também. Todos os valores aromáticos foram quase correspondentes aos teores de três COVs principais, indicando que a fragrância da flor surge verdadeiramente desses componentes de COV. Para entender o mecanismo de formação deste tipo principal de fragrância e sua regulação, primeiramente isolamos um gene de RfBAMT da pétala de Rhododendron fortunei usando clonagem homóloga e tecnologia RACE. O comprimento total de seu cDNA era de 1383 bp, com um quadro de leitura aberto de 1104 bp, codificando um total de 368 aminoácidos. A análise da árvore filogenética mostrou que RfBAMT foi o mais próximo do BSMT de Camellia japonica, pertencente à família das metiltransferases. Em seguida, medimos o nível de expressão de RfBAMT novamente em quatro estágios de desenvolvimento da flor e em diferentes tecidos e folhas de flores. Os resultados mostraram que o nível de expressão deste gene foi altamente correlacionado positivamente com o conteúdo emitido de benzoatos de metila na floração, implicando que RfBAMT desempenha um papel fundamental na formação e regulação de benzoatos de metila em Rhododendron fortune foi clonado e identificado em nosso estudo e seu nível de expressão foi altamente correlacionado positivamente com o conteúdo emitido de benzoatos de metila nas flores e folhas, o que indicou que este gene pode desempenhar um papel importante na regulação da síntese de benzoato de metila em Rhododendron fortunei.

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    Ciência Rural
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      Ciência Rural
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    Authors: Lan Zhang; Yang Wang; Linzi Yang; Jianfeng Chen; +3 Authors

    Hyperspectral image (HSI) classification has become a popular research topic in recent years, and transformer-based networks have demonstrated superior performance by analyzing global semantic features. However, using transformers for pixel-level HSI classification has two limitations: ineffective capture of spatial-spectral correlations and inadequate exploitation of local features. To address these challenges, we propose a dual-dimension self-attention (D2SA) mechanism that fully exploits HIS's high spectral-spatial correlation by using two separate branches to model the global dependence of features from the spectral and spatial dimensions. Additionally, we develop a multilayer residual convolution module that extracts local features and introduces shallow-deep feature interactions to obtain more discriminative representations. Based on these components, we propose a dual-dimension spectral-spatial bottleneck transformer (D2S2BoT) framework for HSI classification that simultaneously models the local interactions and global dependencies of HSI pixels to achieve high-precision classification. By virtue of the D2SA mechanism, the introduced D2S2BOT framework can produce competitive classification results with a limited number of training samples on three well-known datasets, which we hope will provide a strong baseline for future research on transformers in the field of HSI.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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    Authors: Qian Cheng; Xin Li; Taoyang Wang; Boyang Jiang; +3 Authors

    Remote sensing sensor platforms are typically located at a significant distance from the ground, ranging from several hundred meters to hundreds of kilometers. This means that, compared to natural images, remote sensing images (RSI) have larger coverage areas and more complex information. The larger size and data volume of RSI presents challenges for computer vision matching algorithms (MAs), making it difficult to apply them directly to RSI matching. Moreover, a matching framework for multisource RSIs capable of large-scale processing by integrating multiple MAs with the entire RSI as input is presently lacking. This study proposes a tie points (TPs) matching framework of multisource remote sensing images based on the geometric and radiation characteristics of RSI. First, RSI is divided into different grids and undergoes local geometry correction. Next, matching between slice images is performed by MAs. Finally, TPs are generated by mapping matched points in multiple slice images to the whole RSI using a geometric processing model. Six representative MAs including artificial feature MAs and deep learning algorithms are integrated into the framework to match TPs from different RSI. Results demonstrate the extraction of TPs for multisource RSI, validating the framework's efficacy. In addition, a large-scale TPs matching test for deep learning MA is performed by using 13 synthetic aperture radar images (10-m resolution) with TPs root mean square error of 0.368 pixels, further confirming the framework's reliability.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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    Authors: Wensheng Fan; Fan Liu; Jingzhi Li;

    Pansharpening is a fundamental and crucial image processing task for many remote sensing applications, which generates a high-resolution multispectral image by fusing a low-resolution multispectral image and a high-resolution panchromatic image. Recently, vision transformers have been introduced into the pansharpening task for utilizing global contextual information. However, long-range and local dependencies modeling and multiscale feature learning are all essential to the pansharpening task. Learning and exploiting these various information raises a big challenge and limits the performance and efficiency of existing pansharpening methods. To solve this issue, we propose a pansharpening network based on multiscale embedding and dual attention transformers (MDPNet). Specifically, a multiscale embedding block is proposed to embed multiscale information of the images into vectors. Thus, transformers only need to process a multispectral embedding sequence and a panchromatic embedding sequence to efficiently use multiscale information. Furthermore, an additive hybrid attention transformer is proposed to fuse the embedding sequences in an additive injection manner. Finally, a channel self-attention transformer is proposed to utilize channel correlations for high-quality detail generation. Experiments over QuickBird and WorldView-3 datasets demonstrate the proposed MDPNet outperforms state-of-the-art methods visually and quantitatively with low running time. Ablation studies further verify the effectiveness of the proposed multiscale embedding and transformers in pansharpening.

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    Authors: Lele Li; Liejun Wang; Anyu Du; Yongming Li;

    In the field of remote sensing, change detection is a crucial study area. Deep learning has made significant strides in the study of remote sensing image change detection during the past few years. Deep learning techniques still have some drawbacks. The global context cannot be modeled by convolutional neural networks due to the receptive field's restrictions. When extracting visual characteristics, the neural network does not concentrate more on the change region, which results in poor distinction between change and no-change regions. To address these problems, we propose networks with large receptive fields (LRFs) and difference image enhancement. First, we design the LRF strategy. It employs a long kernel shape in one spatial dimension for obtaining a long range of relations. Keeping a narrow kernel size in the other spatial dimension can extract local context information while avoiding interference from irrelevant regions. To focus on the changing features, we design the image difference enhancement (IDE) method, which decreases the distance between invariant features and enlarges the distance between changing features. In addition, we design the cross-channel interaction (CNI) strategy, which models the relationship between feature map channels and extracts feature representations through local CNI. On the CDD, WHU-CD, and LEVIR-CD public datasets, we conducted comprehensive experiments. According to the experimental results, our proposed LRDE-Net performs better than other state-of-the-art change detection techniques, and the change regions are more precisely identified. It can better cope with seasonal changes, light intensity, and other pseudochange disturbances.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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    Authors: Xianxuan Long; Wei Zhuang; Min Xia; Kai Hu; +1 Authors

    With increasingly rapid development of convolutional neural networks, the field of remote sensing has experienced a significant revitalization. However, understanding and detecting surface changes, which necessitate the identification of high-resolution remote sensing images, remain substantial challenges in achieving precise change detection. Excited deep learning-based change detection techniques often exhibit limitations and lack the necessary precision to detect edge details or other nuanced information in remote sensing images. To address these limitations, we propose a unique semantic segmentation deep learning network, the self-adaptive Siamese network (SASiamNet), specifically devised for enhancing change detection in remote sensing images. The SASiamNet excels in real-time land cover segmentation, adeptly extracting local and global information from images via the backbone residual network. Furthermore, it incorporates a primary feature fusion module to extract and fuse the primary stage feature map, and a high-level information refinement module to refine the resultant feature map. This methodology effectively transmutes low-level semantic information into high-level semantic information, thereby improving the overall detection process. Aimed at empirically testing the effectiveness of the SASiamNet, we utilize two distinct datasets: the public dataset, LEVIR-CD, and a challenging dataset, CDD. The latter is composed of bitemporal images sourced from Google Earth, spanning various regions across China. The experiment results unequivocally demonstrate that our approach outperforms traditional methodologies as well as contemporary state-of-the-art change detection techniques, hence underscoring the efficacy of the SASiamNet in the context of remote sensing image change detection.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Yiying Zhang; Yue Huang; Chao Huang; Hailong Huang; +1 Authors

    International audience; Internet of Things (IoT) devices have been widely deployed to build smart cities. How to efficiently collect data from large-scale IoT devices is a valuable and challenging research topic. Benefiting from agility, flexibility, and deployability, an unmanned aerial vehicle (UAV) has great potential to be an aerial base station. However, given the limited battery capacity, the flight time of a UAV is limited. This article focuses on using multi-UAVs to execute long-distance data collection from large-scale IoT devices. We design a multi-UAVs-assisted large-scale IoT data collection system. The core facilities of this system are the data center and charging stations, which are equipped with a limited number of charging piles to provide charging services for UAVs. To ensure the efficient operation of the system, the problem of deployment and flight planning of UAVs is formulated as a joint optimization problem. To solve the problem, a population-based optimization algorithm with a three-layer structure, namely, EDDE-DPDE, is proposed. It includes two core components: 1) elite-driven differential evolution (EDDE) and 2) differential evolution with a dynamic population (DPDE), which are two variants of differential evolution. Thanks to ideas of reusing elite individuals and historical information, the proposed EDDE-DPDE shows an improvement of at least 11.11% compared with four powerful algorithms in terms of average travel time.

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    https://doi.org/10.1109/jiot.2...
    Article . 2024 . Peer-reviewed
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    Authors: Hao Yi; Bo Liu; Bin Zhao; Enhai Liu;

    Due to the limitations of small targets in remote sensing images, such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bidirectional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top–down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground-truth box. Experimental results on NWPU VHR-10, RSOD, and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters, and FPS, and can prove that our modifications made to the original YOLOv8 model are effective.

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    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
    Article . 2024 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
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    Article . 2024
    Data sources: DOAJ
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ IEEE Journal of Sele...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
      Article . 2024 . Peer-reviewed
      License: CC BY NC ND
      Data sources: Crossref
      DOAJ
      Article . 2024
      Data sources: DOAJ
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.