An effective global learning framework for hyperspectral image classification based on encoder-decoder architecture. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Dang, Lanxue; Liu, Chongyang; Dong, Weichuan; Hou, Yane; Ge, Qiang; Liu, Yang
• Is Part Of: International journal of digital earth. Volume 15:Issue 1(2022)
• Page Start: 1350
• Page End: 1376
• Publication Date: 2022-12-31
• Subjects: Deep learning -- global learning -- feature representation -- hyperspectral image classification -- spectral attention; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2022.2108922
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Most deep learning methods in hyperspectral image (HSI) classification use local learning methods, where overlapping areas between pixels can lead to spatial redundancy and higher computational cost. This paper proposes an efficient global learning (EGL) framework for HSI classification. The EGL framework was composed of universal global random stratification (UGSS) sampling strategy and a classification model BrsNet. The UGSS sampling strategy was used to solve the problem of insufficient gradient variance resulted from limited training samples. To fully extract and explore the most distinguishing feature representation, we used the modified linear bottleneck structure with spectral attention as a part of the BrsNet network to extract spectral spatial information. As a type of spectral attention, the shuffle spectral attention module screened important spectral features from the rich spectral information of HSI to improve the classification accuracy of the model. Meanwhile, we also designed a double branch structure in BrsNet that extracted more abundant spatial information from local and global perspectives to increase the performance of our classification framework. Experiments were conducted on three famous datasets, IP, PU, and SA. Compared with other classification methods, our proposed method produced competitive results in training time, while having a greater advantage in test time.