Multi-Label Classification of Remote Sensing Images with Mixed Noise

Remote sensing images usually contain multiple land features and semantic information. Using multi-label learning methods to classify remote sensing images can improve the understanding of image semantics. However, owing to the subjectivity of manual annotation and the complexity of remote sensing image targets, inaccurate image annotation can lead to the introduction of noise (additive noise) or label loss (subtractive noise)—collectively referred to as mixed noise. Their presence can mislead the algorithm training process and reduce classification performance. A multi-label classification algorithm for remote sensing images is proposed to address the issue of mixed noise. First, the images are subjected to strong and weak enhancement transformations, and the two types of enhanced images are fed into two networks with the same structure for collaborative learning. Second, by constraining the consistency within two images and the structural consistency between images and corresponding labels during the training process and then combining the two constraints with Binary Cross Entropy (BCE) loss, the final loss function is formed. Finally, based on the prediction labels, the sorting error is defined to identify and correct noisy labels in the loss function, thereby improving the robustness of the model. To verify the performance of the proposed method, mixed noise multi-label classification experiments are conducted on the remote sensing image multi-label datasets AID, UCM, and DFC15. The proposed method is compared with various multi-label classification methods in terms of image classification indicators and multi-label classification indicators. The results indicate that the overall performance of the proposed method is optimal under different label-to-noise ratios.