A Decompressed Spectral-Spatial Multiscale Semantic Feature Network for Hyperspectral Image Classification

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 18; p. 4642
• Main Authors: Liu, Dongxu, Li, Qingqing, Li, Meihui, Zhang, Jianlin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2023
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Classification; Computer architecture; convolutional neural network; decompressed global spectral-spatial features; Discriminant analysis; Environmental aspects; Environmental monitoring; Feature extraction; Health aspects; hyperspectral image classification; Hyperspectral imaging; Image classification; Machine learning; Methods; Modules; multiscale semantic features; Neural networks; Optimization; Radiation; Semantics; Spectrum analysis; Sustainable agriculture; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15184642

Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model.