Robust Learning of Mislabeled Training Samples for Remote Sensing Image Scene Classification

Abstract

Label information plays an important role in supervised high-resolution remote sensing (HRRS) image scene classification. However, the labels of a dataset are probably unreliable and may contain “noisy” labels. Focusing on uncertain labels problem, a covariance matrix representation-based noisy label model (CMR-NLD) is designed for HRRS image scene classification. The main steps are as follows. First, a pretrained convolutional neural network model is employed to extract the scene images deep features and a principal component analysis based dimensionality reduction method is applied to the first fully connected layer to reduce the computational complexity. Then, the noisy training set is constructed by randomly selecting samples into a specific class from other classes samples. We use this set to simulate the actual situation of tag noise to simulate the actual situation of label noise. Second, the covariance between noisy training samples is calculated to obtain the corresponding covariance matrix, and the average value of the obtained covariance matrix is calculated by rows. As a feature of the matrix form, it can both enlarge the subtle differences between different classes and reduce the visual differences of scene images from the same semantic classes. Then, a decision threshold is set to realize the detection and removal of noisy labels. Finally, the improved training sample set will be evaluated by a support vector machine classifier to demonstrate the proposed detector's effectiveness. Experimental results indicate that the proposed method indeed shows great improvement in noisy label detection of HRRS image scene classification.