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| Research on the Selection Method of Visually Significant Band for Ground Object Classification |
| YANG Guang1, HU Hao-wen1, JIN Chun-bai1, REN Chun-ying2*, WANG Long-guang1, WANG Qi1, LIU Wen-jing1* |
1. Aviation University of Air Force, Changchun 130022, China
2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
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Abstract The selection of remote sensing image bands is a prerequisite for the application of remote sensing data. It aids in the visualization and interpretation of remote sensing images, enhances image quality, and highlights the differences between different surface features. This provides a foundational basis for target recognition, image classification, and change detection. However, the large number of hyperspectral image bands, that is, the high spectral dimension, brings great problems and challenges to the band combination of bloom images. Therefore, it is necessary to reduce the dimension of hyperspectral data. In research on band combination, to preserve the spectral characteristics of the original band, the feature selection method is the most reasonable dimensionality reduction approach. In the original data set, select a specific band to form a band subset, and then carry out band selection research. In this paper, an Improved adjacent subspace partition (IASP) method is designed, and a band selection model based on visual saliency is constructed. Finally, the Histogram-based Contrast algorithm is selected to select the significant band, and a Contrast experiment is designed to verify the effectiveness of the method using the data of the OrbitaHyperSpectral satellite.
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Received: 2024-09-18
Accepted: 2025-07-01
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Corresponding Authors:
REN Chun-ying, LIU Wen-jing
E-mail: renchy@iga.ac.cn; Liuwenjing130@sina.com
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[1] Plaza A, Martinez P, Plaza J, et al. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 466.
[2] Bioucas-Dias J M, Plaza A, Camps-Valls G, et al. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2): 6.
[3] DING Rong-li, LI Jie, SHEN Ji, et al(丁荣莉, 李 杰, 沈 霁, 等). Computer Simulation(计算机仿真), 2021, 38(11): 5.
[4] Itti L, Koch C, Niebur E. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1998, 20(11): 1254.
[5] Hou X, Zhang L. Saliency Detection: A Special Residual Approach, IEEE Conference on Computer Vision and Pattern Recognition, 2007, 1.
[6] Achanta R, Hemami S, Estrada F, et al. Frequency-Tuned Salient Region Detection IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009, 1597.
[7] Cheng M M, Zhang G X, Mitra N J, et al. Global Contrast Based Salient Region Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011.
[8] Corniam M, Baraldil L, Serra, et al. IEEE Transactions on Image Processing, 2018, 27(10): 5142.
[9] Zhang J, Sclaroff S, Lin Z, et al. Unconstrained Salient Object Detection via Proposal Subset Optimization, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, 5733.
[10] PENG Xin-yi, LUO bin, FU Zhi-tao(彭忻怡, 罗 斌, 付志涛). Journal of Yunnan University of Nationalities(云南民族大学学报), 2021, 30(3): 251.
[11] LI Ming-xin, HUANG Yuan-cheng, JING Xia, et al(李茗欣, 黄远程, 竞 霞, 等). Infrared Technology(红外技术), 2023, 45(4): 402.
[12] LI Xian-yi, YAN Jun, YAN Zhi-yu, et al(李先怡, 颜 军, 颜志宇, 等). Satellite Application(卫星应用), 2022, 7: 25.
[13] QIAO Wen-yu, LONG Yi-fan, FU Jie(乔雯钰, 龙亦凡, 付 杰). Beijing Surveying and Mapping(北京测绘), 2020, 34(5): 651.
[14] TANG Chang, WANG Jun(唐 厂, 王 俊). Journal of Tianjin University (Science and Technology)[天津大学学报(自然科学与工程技术版)], 2022, 55(3): 0255.
[15] WANG Qi, YANG Guang, XIANG Ying-jie(王 琪, 杨 桄, 向英杰). Ship Electronic Engineering(舰船电子工程), 2017, 37(4): 98. |
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