A Robust CNN Framework for Change Detection Analysis From Bitemporal Remote Sensing Images

Deep learning (DL) algorithms are currently the most effective methods for change detection (CD) from high-resolution multispectral (MS) remote-sensing (RS) images. Because a variety of satellites are able to provide a lot of data, it is now easy to find changes using efficient DL models. Current CD methods focus on simple structure and combining the features obtained by all the stages together rather than extracting multiscale features from a single stage since it may lead to information loss and an imbalance contribution of features at different stages. This in turn results in misclassification of small changed areas and poor edge and shape preservation of changed areas. This article introduces an enhanced RSCD network (ERSCDNet) for CD from bitemporal aerial and MS images. The proposed encoder–decoder-based ERSCDNet model uses an attention-based encoder and decoder block and a modified new spatial pyramid pooling block at each stage of the decoder part, which effectively utilize features at each encoder stages and prevent information loss. The learning, vision, and remote sensing CD (LEVIR-CD), Onera satellite change detection (OSCD), and Sun Yat-Sen University CD (SYSU-CD) datasets are used to evaluate the ERSCDNet model. The ERSCDNet gives better performance than all the models used in this article for comparison. It gives an F1 score, a Kappa coefficient, and a Jaccard index of (0.9306, 0.9282, 0.8703), (0.8945, 0.8887, 0.8091), and (0.7581, 0.6876, 0.6103) on OSCD, LEVIR-CD, and SYSU-CD datasets, respectively.