ECSPLAIN: Explainability-Constrained Classifier for Pairing the Detection and the Localization of Moving Areas From SAR Interferograms

Detecting slope instabilities on synthetic aperture radar (SAR) interferograms using deep learning approaches presents several challenges. This detection task suffers from the lack of transparency of deep networks, the complexity of the input data (i.e., complex values, sensitivity to distortions, and presence of counterfactuals), and the complexity of the target phenomena (i.e., the variable velocities and the complex underground processes). In this article, we propose a new framework called explainability-constrained classifier for pairing the detection and the localization of moving areas on interferograms (ECSPLAIN), to generate decision, localization, and segmentation maps from a single but explainable classifier network. It consists of training a classifier to detect whether an instability is located in the patch or not, and to explain its decision with a class activation map (CAM) that matches the actual location of the instability. Therefore, by using a single classifier network, the framework can pair the detection and the localization of moving areas. Four CAMs are investigated for the training of the ECSPLAIN framework. Experiments on the ISSLIDE dataset show that our proposal achieves better explainability than standard a posteriori CAMs with more than 0.20 points of improvement in terms of Dice and IoU scores. It also allows competitive performance with segmentation-only networks, with only 0.04 points of difference in terms of Dice and intersection over union (IoU) scores. Thus, the proposed method is competitive with the most efficient methods while being lighter, faster, and delivering a decision based on a human-like reasoning process. Finally, the ECSPLAIN framework is applied to enrich the ISSLIDE dataset, discovering more than 470 manually validated slope instabilities over the Alps.