Following the growth of multisource data with high spatial, spectral, and temporal resolutions, the problem of complex image information mining in remote sensing of environment becomes a great challenge, with many potential applications raising. However, there is no or only a few methodological frameworks for dealing with data with multiple spatial and temporal scales: recognition methods are most often straight applications of standard classification and modelisation methods. Besides, dealing with spatial and temporal neighborhood, with various kinds of data, is expected to improve significantly resulting performances. The goal of the ASTERIX project (Spatio-Temporal Analysis by Recognition within Complex Images for Remote Sensing of Environment) and its originality is to bring new methods, algorithms, softwares in the field of image analysis and machine learning in order to support recognition within complex image, by explicitly dealing with the specificity of remote sensing complex images. In this context, main challenges are related to high dimensionality, heterogeneity, volume and spatio-temporal behaviour of images. Besides methodological achievements supporting the development of the state-of-the-art in image processing and machine learning in the context of recognition within complex images, expected results from the ASTERIX project consist in a set of concrete solutions to crucial problems in remote sensing of environment, and especially in two environment: coastal and montains. More precisely, applications considered are related to the dynamic of environmental objects which help to understand coastal evolution, the dynamic of ash tree colonization in an agricultural mountain landscape, and the dynamic of geological process.