Camouflage Target Recognition Based on Dimension Reduction Analysis of Hyperspectral Image Regions
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Dimensionality Reduction and Principal Component Analysis
2.2. Target Detection Technology
2.3. Method
3. Experimental Results and Analysis
3.1. Data Preparation
3.1.1. Acquisition of Hyperspectral Images
3.1.2. Data Expansion and Model Training
3.2. Further Experiment
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Based on Raw Image Data | The Whole Image after PCA | The Area to Be Measured after PCA | |
---|---|---|---|
SAM | 0.1151 | 0.3501 | 0.8393 |
RMSE | 247.170 | 890.171 | 2169.599 |
Based on Raw Image Data | The Whole Image after PCA | The Area to Be Measured after PCA | |
---|---|---|---|
SAM | 0.1255 | 0.4258 | 0.9107 |
RMSE | 268.503 | 951.987 | 2387.971 |
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Zhao, J.; Zhou, B.; Wang, G.; Liu, J.; Ying, J. Camouflage Target Recognition Based on Dimension Reduction Analysis of Hyperspectral Image Regions. Photonics 2022, 9, 640. https://doi.org/10.3390/photonics9090640
Zhao J, Zhou B, Wang G, Liu J, Ying J. Camouflage Target Recognition Based on Dimension Reduction Analysis of Hyperspectral Image Regions. Photonics. 2022; 9(9):640. https://doi.org/10.3390/photonics9090640
Chicago/Turabian StyleZhao, Jiale, Bing Zhou, Guanglong Wang, Jie Liu, and Jiaju Ying. 2022. "Camouflage Target Recognition Based on Dimension Reduction Analysis of Hyperspectral Image Regions" Photonics 9, no. 9: 640. https://doi.org/10.3390/photonics9090640