Scene distortion detection algorithm using multitemporal remote sensing images

Multitemporal remote sensing images of a particular territory might include accidental scene distortions. Scene distortion is a significant local brightness change caused by the scene overlap with some opaque object or a natural phenomenon coincident with the moment of image capture, for example, clouds and shadows. The fact that different images of the scene are obtained at different instants of time makes the appearance, location and shape of scene distortions accidental. In this article we propose an algorithm for detecting accidental scene distortions using a dataset of multitemporal remote sensing images. The algorithm applies superpixel segmentation and anomaly detection methods to get binary images of scene distortion location for each image in the dataset. The algorithm is adapted to handle images with different spectral and spatial sampling parameters, which makes it more multipurpose than the existing solutions. The algorithm's quality was assessed using model images with scene distortions for two remote sensing systems. The experiments showed that the proposed algorithm with the optimal settings can reach a detection accuracy of about 90% and a false detection error of about 10%.