Cloud Detection in Optical Remote Sensing Images With Deep Semi-Supervised and Active Learning

Clouds hinder the surface observation by optical remote sensing sensors. It is of great significance to detect clouds and nonclouds in remote sensing images. Compared with the traditional cloud detection methods, deep learning methods usually achieve promising detection results. Moreover, large-scale, high-quality labeled datasets can effectively improve the accuracy and generalization of deep learning models. However, this incurs a great deal of label effort and cost. In this letter, we propose a cloud detection method based on deep semi-supervised learning (SSL) and active learning (AL) in optical remote sensing images named SSAL-cloud detection (SSAL-CD). SSL part of SSAL-CD is implemented by the cross-validation between two-way neural networks, which can effectively train in the case of insufficient labeled data and reduce the cost of labeling. AL part proposes a suitable query strategy based on the uncertainty, degree of divergence, and diversity information provided by the training model. The query strategy can screen out high-value samples for SSL, which further improves the detection effect. Repeat the process of SSL and AL until the accuracy meets requirements or the labeling budget is exhausted. Extensive experimental results in Landsat-8 Biome dataset demonstrate that SSAL-CD can achieve the state-of-the-art segmentation performance with a small number of labels.