| 光谱学与光谱分析 |
|
|
|
|
|
| Endmember Extraction Used for Hyperspectral Imagery Loss Compression |
| ZHANG Li-yan, CHEN De-rong, TAO Peng |
| School of Aerospace Science and Technology, Beijing Institute of Technology, Beijing 100081, China |
|
|
|
|
Abstract One of the problems limiting the utility of hyperspectral imagery is how to compress the large number of data effectively. The current methods cannot resolve the problem of the contradiction between large compression rate and spectral information veracious reservation, even the best loss compression method can not bring the satisfying result. The paper presented a loss compression method based on the endmember extraction technology, so as to resolve the contradiction between large compression ratio and spectrum preserved accurately. The endmembers were obtained with vertex component analysis (VCA) and the fractions of them were estimated based on the proportion of cosine angle similitude between endmembers and observed spectrum. The endmembers spectrum and fraction were compressed with the lossless compression method and JPEG2000 loss compression method was used for all of the hyperspectral single-band images to increase compression ratio. The experiment on the AVIRIS data shows that compression ratio was increased greatly and the spectra were resumed effectively. When the compression ratio is 50∶1,the spectrum angle loss is about 2% for most pixels.
|
|
Received: 2007-05-10
Accepted: 2007-08-20
|
|
|
|
Corresponding Authors:
ZHANG Li-yan
E-mail: zhangliyan010@126.com
|
|
［1］ Nella, John. U. S. Patent, 6079665, 2000.
［2］ DU Pei-jun, CHEN Yun-hao, FANG Tao, et al(杜培军，陈云浩，方 涛，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(8)：1171.
［3］ Jose M P Nascimento, Jose M Bioucas Dias. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4)：898.
［4］ David S Taubman, Michael W Marcellin. “JPEG 2000” Image Compression, Fundamentals, Standards and Practice(JPEG 2000图像压缩基础、标准和实践). Translated by WEI Jiang-li, BAI Zheng-yao(魏江力，柏正尧，译). Beijing：Publishing House of Electronics Industry(北京：电子工业出版社), 2004.
［5］ http://aviris.jpl.nasa.gov/.
［6］ Cook S, Harsanyi J, Faber V. Proceedings of SPIE, 2004, 5234：712. |
| [1] |
DAI Ruo-chen1, TANG Huan2*, TANG Bin1*, ZHAO Ming-fu1, DAI Li-yong1, ZHAO Ya3, LONG Zou-rong1, ZHONG Nian-bing1. Study on Detection Method of Foxing on Paper Artifacts Based on
Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1567-1571. |
| [2] |
ZHOU Bing, LI Bing-xuan*, HE Xuan, LIU He-xiong，WANG Fa-zhen. Classification of Camouflages Using Hyperspectral Images Combined With Fusing Adaptive Sparse Representation and Correlation Coefficient[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3851-3856. |
| [3] |
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. |
| [4] |
YANG Bao-hua1, GAO Yuan1, WANG Meng-xuan1, QI Lin1, NING Jing-ming2. Estimation Model of Polyphenols Content in Yellow Tea Based on Spectral-Spatial Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 936-942. |
| [5] |
ZHAO Peng1,2*, HAN Jin-cheng1, WANG Cheng-kun1. Wood Species Classification With Microscopic Hyper-Spectral Imaging Based on I-BGLAM Texture and Spectral Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 599-605. |
| [6] |
ZHAO Fan, YAN Zhao-ru, XUE Jian-xin, XU Bing. Identification of Wild Black and Cultivated Goji Berries by Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 201-205. |
| [7] |
NING Hong-zhang1, 2, TAN Xin1*, LI Yu-hang1, 2, JIAO Qing-bin1, LI Wen-hao1. Joint Space-Spectrum SG Filtering Algorithms for Hyperspectral Images and Its Application[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3699-3704. |
| [8] |
ZHANG Yan, HUA Wen-shen*, HUANG Fu-yu, WANG Qiang-hui, SUO Wen-kai. Space-Spectrum Joint Anomaly Degree for Hyperspectral Anomaly Target Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1902-1908. |
| [9] |
ZHANG Yi-zhuo, XU Miao-miao, WANG Xiao-hu, WANG Ke-qi*. Hyperspectral Image Classification Based on Hierarchical Fusion of Residual Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3501-3507. |
| [10] |
LU Bing1, 2, SUN Jun1*, YANG Ning1, WU Xiao-hong1, ZHOU Xin1. Prediction of Tea Diseases Based on Fluorescence Transmission Spectrum and Texture of Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2515-2521. |
| [11] |
CAI Qing-kong1, LI Er-jun2, JIANG Jin-bao3*, QIAO Xiao-jun3, JIANG Rui-bo1, FENG Hai-kuan4, 5, 6, LIU Shao-tang1, CUI Xi-min3. Study on the Tea Identification of Near-Infrared Hyperspectral Image Combining Spectra-Spatial Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2522-2527. |
| [12] |
LIU Chun-tong, MA Shi-xin*, WANG Hao, WANG Yang, LI Hong-cai. A Density-Based Cluster Kernel RX Algorithm for Hyperspectral Anomaly Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1878-1884. |
| [13] |
HAN Zhong-zhi1, WANG Xuan-hui1,2, SHI Hong-tao1, WAN Jian-hua3*. Mixture End-Members Extraction Method For Coverage Calculation of Sea Oil Spills Based on Hyper-spectral Remote Sensing Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1563-1570. |
| [14] |
GAN Yu-quan1, 2, LIU Wei-hua1, FENG Xiang-peng1, YU Tao1, HU Bing-liang1, WEN De-sheng1. A Sparse Constrained Graph Regularized Nonnegative Matrix Factorization Algorithm for Hyperspectral Unmixing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1118-1127. |
| [15] |
WANG Jie-chao1, 2, 3, SUN Da-peng1, 2, 3, ZHANG Chang-xing1, XIE Feng1, WANG Jian-yu1*. Hyperspectral Image Anomaly Detection Based on Laplasse Constrained Low Rank Representation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(11): 3507-3515. |
|
|
|
|
