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Collecting a large number of reliable training images annotated by multipleland-cover class labels in the framework of multi-label classification istime-consuming and costly in remote sensing (RS). To address this problem,publicly available thematic products are often used for annotating RS imageswith zero-labeling-cost. However, such an approach may result in constructing atraining set with noisy multi-labels, distorting the learning process. Toaddress this problem, we propose a Consensual Collaborative Multi-LabelLearning (CCML) method. The proposed CCML identifies, ranks and correctstraining images with noisy multi-labels through four main modules: 1)discrepancy module; 2) group lasso module; 3) flipping module; and 4) swapmodule. The discrepancy module ensures that the two networks learn diversefeatures, while obtaining the same predictions. The group lasso module detectsthe potentially noisy labels by estimating the label uncertainty based on theaggregation of two collaborative networks. The flipping module corrects theidentified noisy labels, whereas the swap module exchanges the rankinginformation between the two networks. The experimental results confirm thesuccess of the proposed CCML under high (synthetically added) multi-label noiserates. The code of the proposed method is publicly available athttps://noisy-labels-in-rs.org

A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels