Complementary Learning-Based Scene Classification of Remote Sensing Images With Noisy Labels

Abstract

Recently, many deep convolutional neural network (DCNN)-based methods have been proposed for remote sensing (RS) image scene classification (SC). In general, DCNNs obtain good generalization capabilities under the condition of correct labels. Unfortunately, the given samples are sometimes mislabeled. In this letter, the classification of RS images with noisy labels is investigated. First, complementary learning (CL), which learns from complementary labels rather than the original labels, is introduced for RS image classification with noisy labels. CL can decrease the probability of learning from incorrect information, and therefore, it is robust to noisy labels. Then, soft CL, which randomly disturbs the complementary labels of the training samples, is proposed to prevent the overfitting issue in training a DCNN. Moreover, an RS image scene classification framework combining ordinary learning (OL) and CL (RS-COCL) is proposed, which uses CL to obtain a good model and OL to fine-tune the deep model. Additionally, noisy labels filtering is used in RS-COCL (RS-COCL-NLF) to detected and corrected noisy samples. At last, soft CL is used in RS-COCL-NLF to obtain better classification performance. The proposed methods are tested on two widely used datasets (i.e., Northwestern Polytechnical University (NWPU)-RESISC45 and PatternNet) and the obtained results show that the proposed methods provide competitive classification accuracy compared to the state-of-the-art methods.