Abstract
In recent years, benefiting from the rapid development of deep learning technology in the field of computer vision, the study of hyperspectral image (HSI) classification has also made great progress. However, compared with ordinary RGB images, HSIs are more like 3D cubes; therefore, it is necessary and beneficial to explore classification methods suitable for the very special data structure of HSIs. In this paper, we propose Multiple Spectral Resolution 3D Convolutional Neural Network (MSR-3DCNN) for HSI classification tasks. In MSR-3DCNN, we expand the idea of multi-scale feature fusion and dilated convolution from the spatial dimension to the spectral dimension, and combine 3D convolution and residual connection; therefore, it can better adapt to the 3D cubic form of hyperspectral data and make efficient use of spectral information in different bands. Experimental results on four benchmark datasets show the effectiveness of the proposed approach and its superiority as compared with some state-of-the-art (SOTA) HSI classification methods.
Highlights
Hyperspectral Image Classification.Hyperspectral image (HSI) is a hot topic in remote sensing data analysis [1]
Based on spectral dilated convolution (SDC) and spectral multi-scale feature fusion, we propose a Multiple Spectral Resolution (MSR) module to extract the rich spectral information of HSIs
We propose a Multiple Spectral Resolution 3D Convolutional Neural
Summary
Inspired by the research work in [18], we use 3D convolution as the basis of the network to make the classification more suitable to the 3D structure of HSIs. As compared with the 2D convolutional kernel, the 3D convolutional kernel has one more dimension, which can extract spectral information more effectively. Based on SDC and spectral multi-scale feature fusion, we propose a Multiple Spectral Resolution (MSR) module to extract the rich spectral information of HSIs. The module consists of multiple different 3D convolution branches corresponding to multiple different spectrum widths and can extract multi-scale spectral features of HSIs, respectively. We combine the above-mentioned MSR module, SDC, and 3D convolutional and residual connection to propose a Multiple Spectral. Combining the MSR module, SDC, and 3D convolution and residual connection, we propose Multiple Spectral Resolution 3D Convolutional.
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