An improved categorical cross entropy for remote sensing image classification based on noisy labels

Abstract

In recent years, volunteered geographic information (VGI) is widely used to train deep convolutional neural networks (DCNNs) for high-resolution remote sensing image classification. However, noisy labels are often included in the training samples generated by VGI, which inevitably affects the performance of DCNNs. To solve this problem, the negative effect of noisy labels on remote sensing image classification by DCNNs is analyzed. Then, an improved categorical cross-entropy (ICCE) is proposed to address the issue of noisy labels. The ICCE improves the robustness of DCNN to noisy labels by revisiting the sample weighting scheme so that much attention is paid to the clean samples instead of the noisy samples. Besides, the error bound of ICCE is derived with strict mathematical proof under different types of noisy labels, which ensures the advantages of ICCE from theory. Additionally, extensive experiments are conducted on three remote sensing image datasets with simulated and real noisy labels to quantitatively evaluate the performance of ICCE.