| 光谱学与光谱分析 |
|
|
|
|
|
| Independent Component Analysis for Spectral Unmixing in Hyperspectral Remote Sensing Image |
| LUO Wen-fei1, ZHONG Liang2, ZHANG Bing3, GAO Lian-ru3 |
1. School of Geography Science, South China Normal University, Guangzhou 510631, China
2. Institute of Remote Sensing Applications，Chinese Academy of Sciences, Beijing 100101, China
3. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100190, China |
|
|
|
|
Abstract Hyperspectral remote sensing plays an important role in earth observation on land, ocean and atmosphere. A key issue in hyperspectral data exploitation is to extract the spectra of the constituent materials (endmembers) as well as their proportions (fractional abundances) from each measured spectrum of mixed pixel in hyperspectral remote sensing image, called spectral unmixing. Linear spectral mixture model (LSMM) provides an effective analytical model for spectral unmixing, which assumes that there is a linear relationship among the fractional abundances of the substances within a mixed pixel. To be physically meaningful, LSMM is subject to two constraints: the first constraint requires all abundances to be nonnegative and the second one requires all abundances to be summed to one. Independent component analysis (ICA) has been proposed as an advanced tool to unmix hyperspectral image. However, ICA is based on the assumption of mutually independent sources, which violates the constraint conditions in LSMM. This embarrassment compromises ICA applicability to hyperspectral data. To overcome this problem, the present paper introduces a solution of minimization of total correlation of the components. Interestingly, with the minimization of total correlation of the components, the angle of the direction between each components is invariable. A Parallel oblique-ICA (Pob-ICA) algorithm is proposed to correct the angle of the searching direction between the components. Two novelties result from our proposed Pob-ICA algorithm. First, the algorithm completely satisfies the physical constraint conditions in LSMM and overcomes the limitation of statistical independency assumed by ICA. Second, the last component, which is missed in other existing ICA algorithms, can be estimated by our proposed algorithm. In experiments, Pob-ICA algorithm demonstrates excellent performance in the simulative and real hyperspectral images.
|
|
Received: 2009-08-12
Accepted: 2009-11-16
|
|
|
|
Corresponding Authors:
LUO Wen-fei
E-mail: luowenfei@irsa.ac.cn，spdelphi@sohu.com
|
|
[1] TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen(童庆禧, 张 兵, 郑兰芬). Hyperspectral Remote Sensing: Principle, Technology and Application(高光谱遥感: 原理、技术与应用). Beijing: Higher Education Press(北京: 高等教育出版社), 2006. 55.
[2] TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen(童庆禧, 张 兵, 郑兰芬). Hyperspectral Remote Sensing and It’s Multidisciplinary Applications(高光谱遥感的多学科应用). Beijing: Publishing House of Electronics Industry(北京：电子工业出版社), 2006. 262.
[3] Chang Chein-I. Hyperspectral Data Exploitation: Theory and Applications. Hoboken. New Jersey：John Wiley&Sons, Inc., 2007. 2.
[4] Boardman J W, Kruse F A, Green R O. Fifth JPL Airborne Earth Science Workshop. Pasadena，1995. CA: JPI, 1995, 23.
[5] Winter M E. SPIE Imaging Spectrometry V. San Diego CA, 1999. Washington: SPLE Pub, 1999. 266.
[6] Chang C I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer Academic/Plenum Publishers, 2003. 73.
[7] Nascimento J M P, Dias J M B. IEEE Trans. Geosci. Remote Sensing, 2005, 43(4): 898.
[8] ZHANG Li-Yan, CHEN De-rong, TAO Peng(张立燕，谌德荣，陶 鹏). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(7): 1445.
[9] Chang C I, Wu C C, Liu W, et al. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10): 2804.
[10] Bajorski P. IEEE International Geoscience and Remote Sensing Symposium. Anchorage，Alaska: IEEE Press, 2004. 3207.
[11] Zhang Jinkai, Rivard Benoit, Rogge D M. Sensors, 2008，8: 1321.
[12] LUO Wen-fei, ZHONG Liang, LIU Xiang, et al(罗文斐, 钟 亮, 刘 翔，等). Progress in Natural Science(自然科学进展), 2008, 18(11): 1341.
[13] Hyvarinen A, Karhunen J, Oja E. Independent Component Analysis. New York: Wiley, 2001.
[14] Bayliss J, Gualtieri J A, Cromp R. Proceedings of the SPIE symposium on 26th AIPR Workshop: Exploiting New Image Sources and Sensors, 1997 Oct. 15, Washington：The International Society for Optical Engineering Press, 1997，3240: 133.
[15] Chen C，Zhang X. Proceedings of the SPIE symposium on Remote Sensing Conference on Image and Signal Processing for Remote Sensing, Washington：The International Society for Optical Engineering Press, 1999. 3871：150.
[16] Chiang S S, Chang Chein I, Ginsberg I W. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, Honolulu, Howaii, USA, July 24-28, 2000, 3136.
[17] Tu Te Ming. Optical Engineering of SPIE, 2000, 39(4)：897.
[18] Lennon M, Mouchot M, Mercier G, et al. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2001, 6：2896.
[19] Zhang X, Chen C. Optical Engineering, 2002, 41(7)：1717.
[20] Chang C I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer Academic/Plenum Publishers, 2003. 277.
[21] Botchko V, Berina E, Korotkaya Z，et al. Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation, Nara, Japen, April 1-4, 2003. 203.
[22] Keshava Nirmal. Lincoln Laboratory Journal, 2003, 14(1)：55.
[23] Kosaka N, Uto K, Kosugi Y. IEEE of Geoscience and Remote Sensing Letters, 2005, 2(2)：220.
[24] Wang Jing, Chang Chein I. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(9)：0196.
[25] Nascimento J M P. IEEE Transactions on Geoscience and Remote Sensing, 2005, 40(1)：175.
[26] Bakir T, Peter A, Riley R, et al. Proceedings of IEEE International Conference on Image Processing, Atlanta GA USA, October 8-11, 2006. 3237.
[27] Nascimento Jos，Dias Jos Bioucas. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, Barcelona, Spain, July 23-27, 2007. 4033.
[28] ZHANG Xian-da(张贤达). Matrix Analysis And Applications(矩阵分析与应用). Beijing: Tsinghua University Press(北京: 清华大学出版社), 2004. 344.
[29] Abrams M, Ashley R, Rowan L, et al. Geology, 1997, 5: 713.
[30] Resmini R G, Kappus M E, Alirich W S, et al. Intemational Journal of Remote Sensing, 1997, 18(7): 1553.
|
| [1] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
| [2] |
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. |
| [3] |
GAO Yu1, SUN Xue-jian1*, LI Guang-hua2, ZHANG Li-fu1, QU Liang2, ZHANG Dong-hui1, CHANG Jing-jing2, DAI Xiao-ai3. Study on the Derivation of Paper Viscosity Spectral Index Based on Spectral Information Expansion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2960-2966. |
| [4] |
KONG Bo1, YU Huan2*, SONG Wu-jie2, 3, HOU Yu-ting2, XIANG Qing2. Hyperspectral Characteristics and Quantitative Remote Sensing Inversion of Gravel Grain Size in the North Tibetan Plateau[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2381-2390. |
| [5] |
WANG Jin-hua, DAI Jia-le*, LI Meng-qian, LIU Wei, MIAO Ruo-fan. Blind Separation Algorithm of Mixed Minerals Hyperspectral Base on NMF Mode[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2458-2466. |
| [6] |
ZHANG Xia1, WANG Wei-hao1, 2*, SUN Wei-chao1, DING Song-tao1, 2, WANG Yi-bo1, 2. Soil Zn Content Inversion by Hyperspectral Remote Sensing Data and Considering Soil Types[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2019-2026. |
| [7] |
WANG Hui-min1, 2, YU Lei1, XU Kai-lei1, 2, JIANG Xiao-guang1, 2, WAN Yu-qing1, 2*. Estimation of Salt Content of Saline Soil in Arid Areas Based on GF-5 Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2278-2286. |
| [8] |
CAO Yang1, 2, LI Yan-hong1, 2*. Study on the Effects of NO2 Pollution Under COVID-19 Epidemic
Prevention and Control in Urumqi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1981-1987. |
| [9] |
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. |
| [10] |
ZHANG Chao1*, SU Xiao-yu1, XIA Tian2, YANG Ke-ming3, FENG Fei-sheng4. Monitoring the Degree of Pollution in Different Varieties of Maize Under Copper and Lead Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1268-1274. |
| [11] |
XU Long-xin1, 2, 3, 4, SUN Yong-hua2, 3, 4*, WU Wen-huan1, ZOU Kai2, 3, 4, HE Shi-jun2, 3, 4, ZHAO Yuan-ming2, 3, 4, YE Miao2, 3, 4, ZHANG Xiao-han2, 3, 4. Research on Classification of Construction Waste Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3927-3934. |
| [12] |
FENG Tian-shi1, 2, 3, PANG Zhi-guo1, 2, 3*, JIANG Wei1, 2, 3. Remote Sensing Retrieval of Chlorophyll-a Concentration in Lake Chaohu Based on Zhuhai-1 Hyperspectral Satellite[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2642-2648. |
| [13] |
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*. Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1956-1964. |
| [14] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
| [15] |
YANG En1, WANG Shi-bo2*. Study on Directional Near-Infrared Reflectance Spectra of Typical Types of Coal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 847-858. |
|
|
|
|
