Classification of hyperspectral remote sensing images based on three-dimensional convolutional neural networks

Hongli Zhu; Jie Luo; Kai Li; Peng Han; Lvzhou Li

doi:10.1117/12.3045900

20 September 2024 Classification of hyperspectral remote sensing images based on three-dimensional convolutional neural networks

Hongli Zhu, Jie Luo, Kai Li, Peng Han, Lvzhou Li

Author Affiliations +

Proceedings Volume 13269, Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024); 1326903 (2024) https://doi.org/10.1117/12.3045900
Event: Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In recent years, with the rapid development of hyperspectral remote sensing technology, hyperspectral remote sensing data has witnessed progressive utilization in the subway transportation industry. Due to the large amount of redundant information in hyperspectral images and the high correlation between band information, traditional classification algorithms often find it challenging to obtain more accurate classification information. Convolutional neural networks in the field of subway transportation have achieved great success in hyperspectral remote sensing image classification. These networks operate quickly and offer high classification accuracy, thereby greatly advancing hyperspectral remote sensing image classification technology and benefiting the subway industry. This article analyzes and compares the classification accuracy of two hyperspectral remote sensing image classification methods based on three-dimensional convolutional neural networks, capitalizing on the Pavia University and Indian Pines datasets. It specifically examines the classification of transportation infrastructure, particularly subway lines, within these datasets. The two models differ in data input sizes and convolutional layer parameters. By varying the sample size and epochs for analysis, we assess the results using the Kappa coefficient and accuracy to evaluate the models’ classification performance. The findings indicate that increasing the data input size and period can effectively enhance the classification accuracy of subway lines and other land features. Notably, increasing the data input size leads to a more significant improvement, while the effect of increasing the number of epochs has a certain threshold beyond which improvements diminish.

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

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Hongli Zhu, Jie Luo, Kai Li, Peng Han, and Lvzhou Li "Classification of hyperspectral remote sensing images based on three-dimensional convolutional neural networks", Proc. SPIE 13269, Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), 1326903 (20 September 2024); https://doi.org/10.1117/12.3045900

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KEYWORDS

Data modeling

3D modeling

Convolution

Image classification

Hyperspectral imaging

Feature extraction

Remote sensing

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