Binary feature learning with local spectral context-aware attention for classification of hyperspectral images

Abstract

The classification of hyperspectral images (HSIs) has achieved success in applications. For many approaches, features are directly extracted from whole spectral pixels, which can not well describe local characteristics. These methods are also susceptible to noise since each feature code is learned individually. Accordingly, a binary feature learning method with local spectral context-aware attention (BFLSC) is proposed for the classification. Specifically, for training samples, we first build the local spectrum models (LSMs) to describe local spectral properties, where each training sample is segmented into some parts and the difference between the central value and its neighborhoods is calculated in each part. Then, we construct the BFLSC model to learn a projection and binary features of training samples. In such model, the spectral context-awareness attention is established to collaboratively learn binary feature codes by enforcing one shift between 0/1 of each LSM, which enhances the robustness and stability of binary leaning. We also introduce the loss constraint, even distribution constraint, and variance constraint to reduce information loss and improve the quality of learned feature distribution. Additionally, an optimization scheme is designed to obtain the solution of the BFLSC model. Further, the learned binary features are added to train the support vector machine (SVM). For each testing sample, the LSMs are first extracted, and then mapped into binary features by the learned projection. The trained SVM is finally used for the mapped binary features to predict the label of the testing sample. Experimental results validate that our BFLSC realizes the better performance compared with some advanced approaches.