LAUSR

Currently, the most advanced high-resolution remote sensing image (HRRSI) semantic labeling methods rely on deep neural networks. However, HRRSIs naturally have a serious class imbalance problem, which is not yet well solved by the current method. The cross-entropy loss is often used to guide the training of semantic labeling neural networks for HRRSIs, but it is essentially dominated by the major classes in the image, resulting in poor predictions for the minority class. Based on the prediction results, focal loss (FL) effectively suppresses the negative impact of class imbalance in dense object detection by redistributing the loss of each sample. In this article, we thoroughly analyze the inadequacy of FL for semantic labeling, which inevitably introduces confusing-classified examples that are more difficult to classify while suppressing the loss of well-classified examples. Therefore, following the core idea of FL, we redefine the hard examples in semantic labeling of HRRSIs and propose the prediction confusion map to measure the classification difficulty. Based on this, we further propose the calibrated focal loss (CFL) for the semantic labeling of HRRSIs. Finally, we conduct complete experiments on the International Society for Photogrammetry and Remote Sensing Vaihingen and Potsdam datasets to analyze the semantic labeling performance, model uncertainty, and confidence calibration of different loss functions. Experimental results show that CFL can achieve outstanding results compared with other commonly used loss functions without increasing model parameters and training iterations, demonstrating the effectiveness of our method. In the end, combined with our previously proposed HCANet, we further verify the effectiveness of CFL on state-of-the-art network structures.