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

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Abstract
Deep learning (DL) algorithms are currently the most effective methods for change detection (CD) from high-resolution multi-spectral (MS) remote-sensing (RS) images. Because a variety of satellites are able to provide a lot of data, it is now easy to find out 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 multi-scale features from a single stage since it may lead to information loss and imbalance contribution of features at different stages. This in turn results in misclassification of small changed areas, poor edge and shape preservation of changed areas. This paper introduces an Enhanced RSCD network (ERSCDNet) for CD from bi-Temporal (BT) aerial and MS images. The proposed encoder-decoder based ERSCDNet model uses an attention based encoder and decoder blocks and a Modified New Spatial Pyramid Pooling block (MNSPP) 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 paper for comparison. It gives a F1 Score (F1), a Kappa Coefficient (Ka), and a Jaccard Index (JI) 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.
Funding Information
  • RESPOND Scheme of Indian Space Research Organization (ISRO/RES/4/683/19-20)

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