| 光谱学与光谱分析 |
|
|
|
|
|
| Orthogonal Projection Divergence-Based Hyperspectral Band Selection |
| SU Hong-jun1,2，SHENG Ye-hua1*，Yang He2，Du Qian2 |
1. Key Lab of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing 210046, China
2. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39759, USA |
|
|
|
|
Abstract Due to the high data dimensionality of a hyperspectral image, dimensionality reduction algorithm has attracted much attention in hyperspectral image analysis. Band selection algorithm, which selects appropriate bands from the original set of spectral bands, can preserve original information from the data and is useful for image classification and recognition. In the present paper, a novel band selection algorithm based on orthogonal projection divergence (OPD) is proposed, it aims to discriminate the interesting objects from background and noise information, maximize the spectral similarity between different spectral vectors by projecting the original data to feature space. Two HYDICE Washington DC Mall images and an HYMAP Purdue campus image data were experimented, and support vector machine (SVM) classifier was used for classification. The selected band number varies from 5 to 40 in order to study the impacts of different band selection algorithms on different features. For the computation complex, the sequential floating forward search (SFFS) was used to get the appropriate bands. The experiments have proved that our proposed OPD algorithm can outperform other traditional band selection methods such as SAM, ED, SID, and LCMV-BCC for hyperspectral image analysis. It is proven that OPD band selection is effective and robust in hyperspectral remote sensing dimensionality reduction.
|
|
Received: 2010-04-14
Accepted: 2010-07-18
|
|
|
|
Corresponding Authors:
SHENG Ye-hua
E-mail: shengyehua@njnu.edu.cn
|
|
[1] Chang C I. Hyperspectral Imaging: Signal Processing Algorithm Design and Analysis, NJ: John Wiley & Sons, 2009. 1.
[2] Hughes G F. IEEE Trans. Information Theory, 1968, IT-14: 55.
[3] Chang C I, Du Q. IEEE Trans. Geoscience Remote Sensing, 2004, 42(3): 608.
[4] Nascimento J, Dias J. IEEE Trans. Geoscience Remote Sensing, 2005, 43(4): 898.
[5] Nadakuditi R R, Edelman A. IEEE Trans. Signal Processing, 2008, 56(7): 2625.
[6] Bioucas-Dias J M, Nascimento J M P. IEEE Trans. Geoscience Remote Sensing, 2008, 46(8): 2435.
[7] Debba P, Cho M, Mathieu R. Pro. Whispers, 2009, 5289068: 1.
[8] Keshava N. IEEE Trans. Geoscience Remote Sensing, 2004, 42(7): 1552.
[9] Chang C I. IEEE Trans. on Information Theory, 2000, 46(5): 1927.
[10] Chang C I, Wang S. IEEE Trans. Geoscience Remote Sensing, 2006, 44(6): 1575.
[11] Su H J, Sheng Y H, Du P J. Proceedings of the ISPRS 2008. Vol. XXXVII. Part B7: 279.
[12] Bajcsy P, Groves P. Photogrammetric Engineering & Remote Sensing, 2004, 70: 793.
[13] SU Hong-jun, DU Pei-jun, SHENG Ye-hua(苏红军, 杜培军, 盛业华). Application Research of Computers(计算机应用研究), 2008, 25(4): 1093.
[14] Harsanyi J C, Chang C I. IEEE Trans. Geoscience Remote Sensing, 1994, 32: 779.
[15] Chang C I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer, 2003. 15.
[16] Pudil P, Novovicová J, Kittler J. Pattern Recognition Letters, 1994, 15: 1119.
[17] Du Q, Yang H. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 564.
[18] Burges C J C. Data Mining and Knowledge Discovery, 1998, 2: 121.
|
| [1] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [2] |
YANG Guang1, JIN Chun-bai1, REN Chun-ying2*, LIU Wen-jing1, CHEN Qiang1. Research on Band Selection of Visual Attention Mechanism for Object
Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 266-274. |
| [3] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [4] |
JIN Chun-bai1, YANG Guang1*, LU Shan2*, LIU Wen-jing1, LI De-jun1, ZHENG Nan1. Band Selection Method Based on Target Saliency Analysis in Spatial Domain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2952-2959. |
| [5] |
SONG Ruo-xi1, 3, FENG Yi-ning3, CHENG Wei2, WANG Xiang-hai2, 3*. Advance in Hyperspectral Images Change Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2354-2362. |
| [6] |
HU Wen-feng1, 2, TANG Wei-hao1, LI Chuang1, WU Jing-jin1, MA Qing-fen1, LUO Xiao-chuan1, WANG Chao2, TANG Rong-nian1*. Estimating Nitrogen Concentration of Rubber Leaves Based on a Hybrid Learning Framework and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2050-2058. |
| [7] |
ZHU Ling, QIN Kai*, SUN Yu, LI Ming, ZHAO Ying-jun. Hyperspectral Unmixing Based on Deep Stacked Autoencoders Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1508-1516. |
| [8] |
PAN Zhao-jie1, SUN Gen-yun1, 2*, ZHANG Ai-zhu1, FU Hang1, WANG Xin-wei3, REN Guang-wei3. Tobacco Disease Detection Model Based on Band Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1023-1029. |
| [9] |
YUAN Zhuang1, DONG Da-ming2*. Near-Infrared Spectroscopy Measurement of Contrastive Variational Autoencoder and Its Application in the Detection of Liquid Sample[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3637-3641. |
| [10] |
BAI Zi-jin1, PENG Jie1*, LUO De-fang1, CAI Hai-hui1, JI Wen-jun2, SHI Zhou3, LIU Wei-yang1, YIN Cai-yun1. A Mid-Infrared Spectral Inversion Model for Total Nitrogen Content of Farmland Soil in Southern Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2768-2773. |
| [11] |
ZHANG Liu, ZHANG Jia-kun, LÜ Xue-ying, SONG Hong-zhen, WANG Wen-hua*. Research on Tunable Spectrum Reconstruction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1378-1384. |
| [12] |
KONG Yu-ru1, 2, WANG Li-juan1*, FENG Hai-kuan2, XU Yi1, LIANG Liang1, XU Lu1, YANG Xiao-dong2*, ZHANG Qing-qi1. Leaf Area Index Estimation Based on UAV Hyperspectral Band Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 933-939. |
| [13] |
YANG Bao-hua, GAO Zhi-wei, QI Lin, ZHU Yue, GAO Yuan. Prediction Model of Soluble Solid Content in Peaches Based on Hyperspectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3559-3564. |
| [14] |
YANG Zhi-chao1, 2, SHI Lu1, CAI Jing1, ZHANG Hui1. Raman Spectral Blood Stain Identification Based on Band Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3137-3141. |
| [15] |
ZHANG Liu1, YE Nan1, MA Ling-ling2, WANG Qi2, LÜ Xue-ying1, ZHANG Jia-bao1*. Hyperspectral Band Selection Based on Improved Particle Swarm Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3194-3199. |
|
|
|
|
