Sparsity-Based Methods for Classification

Wu, Zebin; Xu, Yang; Liu, Jianjun

doi:10.1007/978-3-030-38617-7_8

Cited by 2 publications

(1 citation statement)

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“…Although the above methods have tremendous advantages over traditional machine learning in extracting hyperspectral spatial-spectral features, obtaining spatial and spectral information using neural networks is still a considerable challenge [59]- [63]. First, because the HSI datasets have been extensively studied, the number of available training samples and test samples is relatively small.…”

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confidence: 99%

3-D Channel and Spatial Attention Based Multiscale Spatial–Spectral Residual Network for Hyperspectral Image Classification

Bin

Sun

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the rapid development of aerospace and various remote sensing platforms, the amount of data related to remote sensing is increasing rapidly. To meet the application requirements of remote sensing big data, an increasing number of scholars are combining deep learning with remote sensing data. In recent years, based on the rapid development of deep learning methods, research in the field of hyperspectral image (HSI) classification has seen continuous breakthroughs. In order to fully extract the characteristics of HSIs and improve the accuracy of image classification, this article proposes a novel 3-D channel and spatial attention-based multiscale spatial-spectral residual network (termed CSMS-SSRN). The CSMS-SSRN framework uses a three-layer parallel residual network structure by using different 3-D convolutional kernels to continuously learn spectral and spatial features from their respective residual blocks. Then, the extracted depth multiscale features are stacked and input into the 3-D attention module to enhance the expressiveness of the image features from the two aspects of channel and spatial domains, thereby improving the accuracy of classification. The CSMS-SSRN framework proposed in this article can achieve better classification performance on different HSI datasets.

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confidence: 99%