Change detection is a challenging task that has received much attention in the remote sensing field. Whereas numerous remote sensing change detection methods have been developed, the efficiency of these approaches is insufficient to meet the real-world applications’ requirements. Recently, deep learning methods have been largely used for remote sensing imagery change detection, most of these approaches are limited by their training dataset. However, adapting a pretrained convolutional neural network (CNN) on an image classification task to change detection is extremely challenging. An automatic land cover/use change detection approach based on fast and accurate frameworks for optical high-resolution remote sensing imagery is proposed. The fast framework is designed for applications that require immediate results with less complexity. The accurate framework is designed for applications that require high levels of precision, it decomposes large images into small processing blocks and forwards them into CNN. The proposed frameworks can learn transferable features from one task to another and escape the use of the expensive and inaccurate handcrafted features and the requirements of the big training dataset. A number of experiments were carried out to validate the proposed approach on three real bitemporal images. The experimental results illustrate the superiority of the proposed approach over other state-of-the-art methods.
The use of the national very high resolution space system Alsat-2A is a primordial task having a significant technological and economical interest assuring the strengthening autonomy in terms of availability and coverage in the satellite data. Also it allows us to improve and update the base and thematic mapping throughout the national territory. Firstly, the characteristics of ALSAT-2A are presented, namely the images and the imaging system with a brief history of ALSAT program. Secondly, as a prerequisite, knowing the internal parameters is essentials to modelize the geometry of such imaging system. From metadata given by the images distributor and ground control points, several test are described and the results are presented. The test data are supplied by ASAL (Algerian Space Agency), the first dataset comprise a panchromatic image over the region of El Bayadh in the North West of Algeria equipped with nine GPS surveyed points. The second dataset is an along track stereoscopic panchromatic 1A level images over the town of sevilla in the south of Spain with 24 GCPs. Finally, a discussion on obtained results is dressed showing the geometric capability of ALSAT-2A.