Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification
Hyperspectral remote sensing technology is swiftly evolving, prioritizing affordability, enhanced portability, seamless integration, sophisticated intelligence, and immediate processing capabilities. The leading model for classifying hyperspectral images, which relies on convolutional neural network...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4398 |
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| author | Xuebin Tang Ke Zhang Xiaolei Zhou Lingbin Zeng Shan Huang |
| author_facet | Xuebin Tang Ke Zhang Xiaolei Zhou Lingbin Zeng Shan Huang |
| author_sort | Xuebin Tang |
| collection | DOAJ |
| description | Hyperspectral remote sensing technology is swiftly evolving, prioritizing affordability, enhanced portability, seamless integration, sophisticated intelligence, and immediate processing capabilities. The leading model for classifying hyperspectral images, which relies on convolutional neural networks (CNNs), has proven to be highly effective when run on advanced computing platforms. Nonetheless, the high degree of parameterization inherent in CNN models necessitates considerable computational and storage resources, posing challenges to their deployment in processors with limited capacity like drones and satellites. This paper focuses on advancing lightweight models for hyperspectral image classification and introduces EBCNN, a novel binary convolutional neural network. EBCNN is designed to effectively regulate backpropagation gradients and minimize gradient discrepancies to optimize BNN performance. EBCNN incorporates an adaptive gradient scaling module that utilizes a multi-scale pyramid squeeze attention (PSA) mechanism during the training phase, which can adjust training gradients flexibly and efficiently. Additionally, to address suboptimal training issues, EBCNN employs a dynamic curriculum learning strategy underpinned by a confidence-aware loss function, Superloss, enabling progressive binarization and enhancing its classification effectiveness. Extensive experimental evaluations conducted on five esteemed public datasets confirm the effectiveness of EBCNN. These analyses highlight a significant enhancement in the classification accuracy of hyperspectral images, achieved without incurring additional memory or computational overheads during the inference process. |
| format | Article |
| id | doaj-art-8e3f9fa10f7b411fb4a86d68c9eabe2a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-8e3f9fa10f7b411fb4a86d68c9eabe2a2025-08-20T01:55:45ZengMDPI AGRemote Sensing2072-42922024-11-011623439810.3390/rs16234398Enhancing Binary Convolutional Neural Networks for Hyperspectral Image ClassificationXuebin Tang0Ke Zhang1Xiaolei Zhou2Lingbin Zeng3Shan Huang4National University of Defense Technology, ChinaNational University of Defense Technology, ChinaNational University of Defense Technology, ChinaNational University of Defense Technology, ChinaNational University of Defense Technology, ChinaHyperspectral remote sensing technology is swiftly evolving, prioritizing affordability, enhanced portability, seamless integration, sophisticated intelligence, and immediate processing capabilities. The leading model for classifying hyperspectral images, which relies on convolutional neural networks (CNNs), has proven to be highly effective when run on advanced computing platforms. Nonetheless, the high degree of parameterization inherent in CNN models necessitates considerable computational and storage resources, posing challenges to their deployment in processors with limited capacity like drones and satellites. This paper focuses on advancing lightweight models for hyperspectral image classification and introduces EBCNN, a novel binary convolutional neural network. EBCNN is designed to effectively regulate backpropagation gradients and minimize gradient discrepancies to optimize BNN performance. EBCNN incorporates an adaptive gradient scaling module that utilizes a multi-scale pyramid squeeze attention (PSA) mechanism during the training phase, which can adjust training gradients flexibly and efficiently. Additionally, to address suboptimal training issues, EBCNN employs a dynamic curriculum learning strategy underpinned by a confidence-aware loss function, Superloss, enabling progressive binarization and enhancing its classification effectiveness. Extensive experimental evaluations conducted on five esteemed public datasets confirm the effectiveness of EBCNN. These analyses highlight a significant enhancement in the classification accuracy of hyperspectral images, achieved without incurring additional memory or computational overheads during the inference process.https://www.mdpi.com/2072-4292/16/23/4398remote sensinghyperspectral image classificationbinary neural networkconvolutional neural networkdynamic curriculum learningpyramid squeeze attention |
| spellingShingle | Xuebin Tang Ke Zhang Xiaolei Zhou Lingbin Zeng Shan Huang Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification Remote Sensing remote sensing hyperspectral image classification binary neural network convolutional neural network dynamic curriculum learning pyramid squeeze attention |
| title | Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification |
| title_full | Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification |
| title_fullStr | Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification |
| title_full_unstemmed | Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification |
| title_short | Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification |
| title_sort | enhancing binary convolutional neural networks for hyperspectral image classification |
| topic | remote sensing hyperspectral image classification binary neural network convolutional neural network dynamic curriculum learning pyramid squeeze attention |
| url | https://www.mdpi.com/2072-4292/16/23/4398 |
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