<p>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.</p>
With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.