Lightweight multiscale dense network hyperspectral remote sensing image classification based on 3D Gabor filters

Dan Xue; Chaozhu Zhang; Ru Zhao

doi:10.1117/12.3028953

22 May 2024 Lightweight multiscale dense network hyperspectral remote sensing image classification based on 3D Gabor filters

Dan Xue, Chaozhu Zhang, Ru Zhao

Author Affiliations +

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317608 (2024) https://doi.org/10.1117/12.3028953
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Multiscale Dense Network (MSDN) is a deep convolutional neural network method proposed to solve the loss of fine features in hyperspectral remote sensing images. Multiscale Dense Network (MSDN) makes full use of the different scale information in the network to realize the feature extraction of HSI in both horizontal and vertical dimensions. Since MSDN maintains all fine scales until the last layer of the network, the computation is large. To address this problem, this paper proposes a hyperspectral image classification method based on lightweight multi-scale dense network (LMSDN). It uses dividing the MSDN network into S blocks along the horizontal dimension, keeping the coarsest (S-i+1) scale only in the ith block, and only down sampling the final output of each block. Based on the LMSDN network, in order to maintain the classification accuracy of the network, the 3D Gabor filter is combined with the LMSDN, and the 3D Gabor filter is first used to extract some internal structural information such as orientation and size of the hyperspectral remote sensing image, which is then sent to the LMSDN network for the classification of HSI. Experiments are conducted on two publicly available datasets, and the experimental results are compared with other methods, which proves that the Gabor-LMSDN network has less computation and shortens the experimental time compared with the MSDN network while guaranteeing the classification accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Dan Xue, Chaozhu Zhang, and Ru Zhao "Lightweight multiscale dense network hyperspectral remote sensing image classification based on 3D Gabor filters", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317608 (22 May 2024); https://doi.org/10.1117/12.3028953

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KEYWORDS

Hyperspectral imaging

Image classification

Remote sensing

Image filtering

Tunable filters

3D image processing

Feature extraction

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