高光谱遥感图像目标探测与分类技术研究
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摘要
高光谱遥感图象目标探测与分类技术是高光谱遥感理论与应用研究的重要环节。本文从光谱的表达方式、无监督聚类方式、样本分布概率、高光谱图象在特征空间的几何结构以及在图象空间的连续性入手在特征提取、无监督分类、端元提取、线性解混、目标探测、异常探测等方面得到了如下结论:
1.提出了一种基于光谱重排的特征提取方法。理论及实践证明,任何两种不同地物的光谱通过光谱重排之后,总有显著的相对特征出现。这对具有相似波形不同地物的特征提取乃至进一步的分析与处理无疑大有裨益。
2.提出了空间连续性的概念及其定量描述并将其成功的应用于图象分类、光谱优化、去冗余以及端元提取的实时处理。
3.提出了一种基于万有引力的非监督自组织聚类算法。把需要分类的特征空间中的各个样本比做宇宙中的星球,在其间的万有引力作用下产生运动,最后所形成的各个大的星系即对应着我们所需要的分类结果。
4.提出了两种自动提取图象端元的算法。根据高光谱图象在其特征空间中的单形体结构,引入了高维解析几何及施密特正交化,提出了最大距离自动提取端元的算法;引入了一种新的与数据维数无关的求取高维单形体体积的公式,将其应用于端元的自动提取,克服了N-FINDR算法受数据维数限制的固有缺陷;并提出了端元结构函数的概念,使得端元的重要性取决于它对单形体结构的影响而不是其在图象中信息量的多少。这对小目标提取显然有着重大的意义。
5.提出了基于端元投影向量的目标提取算法。利用施密特正交化过程,由图象中的每个端元均可以向别的所有端元构成的超平面做一垂线,在此垂线上的投影即可得到在不考虑信息量的基础上此端元为前景、别的端元为背景的最大区分效果。而以此垂线指向为方向的单位向量我们称之为端元投影向量。
6.提出了一种基于单形体结构本质属性的几何定理并将其应用于线性解混。把图象中任何一个混合象元内部各个端元所占的比例归结为一个简单的体积比,并且给出了证明。无论从理论上,还是在应用上,此结论都有着重要的意义。
7.提出了加权相关矩阵(协方差矩阵)的思想。小目标探测与异常目标探测都是根据图象的统计特性从信息量分布的角度对图象中低概率目标进行探测与分
Target detection and classification is one of primary tasks of hyperspectral imaging. In terms of the method of spectral expression, the style of unsupervised cluster, probability in the data, the geometrical construction of hyperspectral imaging in the band space and the it's continuity in the image space, the dissertation draws some conclusion on feature extraction, unsupervised classification, endmember selection, linear unmixing, target detection and anomaly detection as follows:1. A method for spectral feature extraction was developed based on spectral recomposition. By arranging the spectra by the sort of their reflectance or DN, the spectral curves that are originally difficult to be extracted features from will usually produce some obvious features. It is helpful to feature extraction and father analysis and process.2. The concept of spatial continuity was proposed and successfully applied to image classification, spectral optimization, redundancy reduction and real-time endmember determination.3. A unsupervised classification method was proposed based on universal gravitation. Each pixel that was taken as a star in the universe would move with the gravitation of all the other pixels. The last formed galaxy is corresponding to the result of classification.4. Two approaches of autonomous spectral endmember determination were developed. Based on the convex nature of hyperspectral data in its characteristic space, Gram-Schmidt Orthonormalization process, high dimensional analytic geometry and distance between pixels were introduce to find a unique set of purest pixels in an image; A new volume formula of simplex which was independent of dimension of the data was introduced to find all the endmembers which are larger than any other volume formed from any other combination of pixels. The concept of endmember constrution function was first proposed, so the weightiness of each endmember is depended on it's influence to the simplex contraction, but not it's information magnitude. It's significant to the small target extraction.5. A method of target extraction based on endmember projection vector was developed. Based on the convex nature of hyperspectral data in its band space, a series of vectors named endmember projection vector are produced for use of object extraction. The technique is based on the fact that in band space, any endmember is the farthest point from the hyperplane consisted of all the other endmembers.6. A new theorem about the nature of simplex was proposed and applied to spectral linear unmixing. Once all the endmembers were found, the image cube can be "unmixed" into fractional abundances of each material in each pixel by a simple ratio of volume.
1.提出了一种基于光谱重排的特征提取方法。理论及实践证明,任何两种不同地物的光谱通过光谱重排之后,总有显著的相对特征出现。这对具有相似波形不同地物的特征提取乃至进一步的分析与处理无疑大有裨益。
2.提出了空间连续性的概念及其定量描述并将其成功的应用于图象分类、光谱优化、去冗余以及端元提取的实时处理。
3.提出了一种基于万有引力的非监督自组织聚类算法。把需要分类的特征空间中的各个样本比做宇宙中的星球,在其间的万有引力作用下产生运动,最后所形成的各个大的星系即对应着我们所需要的分类结果。
4.提出了两种自动提取图象端元的算法。根据高光谱图象在其特征空间中的单形体结构,引入了高维解析几何及施密特正交化,提出了最大距离自动提取端元的算法;引入了一种新的与数据维数无关的求取高维单形体体积的公式,将其应用于端元的自动提取,克服了N-FINDR算法受数据维数限制的固有缺陷;并提出了端元结构函数的概念,使得端元的重要性取决于它对单形体结构的影响而不是其在图象中信息量的多少。这对小目标提取显然有着重大的意义。
5.提出了基于端元投影向量的目标提取算法。利用施密特正交化过程,由图象中的每个端元均可以向别的所有端元构成的超平面做一垂线,在此垂线上的投影即可得到在不考虑信息量的基础上此端元为前景、别的端元为背景的最大区分效果。而以此垂线指向为方向的单位向量我们称之为端元投影向量。
6.提出了一种基于单形体结构本质属性的几何定理并将其应用于线性解混。把图象中任何一个混合象元内部各个端元所占的比例归结为一个简单的体积比,并且给出了证明。无论从理论上,还是在应用上,此结论都有着重要的意义。
7.提出了加权相关矩阵(协方差矩阵)的思想。小目标探测与异常目标探测都是根据图象的统计特性从信息量分布的角度对图象中低概率目标进行探测与分
Target detection and classification is one of primary tasks of hyperspectral imaging. In terms of the method of spectral expression, the style of unsupervised cluster, probability in the data, the geometrical construction of hyperspectral imaging in the band space and the it's continuity in the image space, the dissertation draws some conclusion on feature extraction, unsupervised classification, endmember selection, linear unmixing, target detection and anomaly detection as follows:1. A method for spectral feature extraction was developed based on spectral recomposition. By arranging the spectra by the sort of their reflectance or DN, the spectral curves that are originally difficult to be extracted features from will usually produce some obvious features. It is helpful to feature extraction and father analysis and process.2. The concept of spatial continuity was proposed and successfully applied to image classification, spectral optimization, redundancy reduction and real-time endmember determination.3. A unsupervised classification method was proposed based on universal gravitation. Each pixel that was taken as a star in the universe would move with the gravitation of all the other pixels. The last formed galaxy is corresponding to the result of classification.4. Two approaches of autonomous spectral endmember determination were developed. Based on the convex nature of hyperspectral data in its characteristic space, Gram-Schmidt Orthonormalization process, high dimensional analytic geometry and distance between pixels were introduce to find a unique set of purest pixels in an image; A new volume formula of simplex which was independent of dimension of the data was introduced to find all the endmembers which are larger than any other volume formed from any other combination of pixels. The concept of endmember constrution function was first proposed, so the weightiness of each endmember is depended on it's influence to the simplex contraction, but not it's information magnitude. It's significant to the small target extraction.5. A method of target extraction based on endmember projection vector was developed. Based on the convex nature of hyperspectral data in its band space, a series of vectors named endmember projection vector are produced for use of object extraction. The technique is based on the fact that in band space, any endmember is the farthest point from the hyperplane consisted of all the other endmembers.6. A new theorem about the nature of simplex was proposed and applied to spectral linear unmixing. Once all the endmembers were found, the image cube can be "unmixed" into fractional abundances of each material in each pixel by a simple ratio of volume.
引文
1.童庆禧,1990,遥感信息获取技术的研究与发展,遥感应用的实践与创新,测绘出版社,pp50-55。
2.童庆禧,1995,电子光学遥感系统的过去、现在和未来,成象光谱技术发展初探,遥感科学新进展,科学出版社,pp51-60。
3.童庆禧,郑兰芬,1999,高光谱遥感发展现状,遥感知识创新文集。中国科学技术出版社,pp13-25。
4.刘建贵.高光谱城市地物及人工目标识别与提取.中国科学院博士论文.1999.
5. Bateson, C.A. and Curtiss, B. A tool for manual endmember selection and spectral Unfixing. in Summaries of the V JPL Airborne Earth Science Workshop, Pasadena, CA, 1993.
6. Bateson, C.A., Asner, G. P., Wessman, C.A., "Endmember Bundles: A New Approach to Incorporating Endmember Variability into Spectral Mixture Analysis," IEEE Transactions on Geoscience and Remote Sensing, vol. 38 issue 2 part: 2, pp. 1083-1094, March 2000.
7. Boardman, J. W., 1989, Inversion of imaging spectrometry data using singular value decomposition: in Proceedings, IGARSS'89, 12th Canadian Symposium on Remote Sensing, 4, pp. 2069-2072.
8. Boardman, J. W., 1993, Automated spectral unmixing of AVIRIS data using convex geometry concepts: in Summaries, Fourth JPL Airborne Geoscience Workshop, JPL Publication 93-26, v. 1, p. 11-14.
9. Boardman, J. W. (1998). Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering. Summaries of the seventh JPL Airborne Geoscience Workshop, JPL Publication 97-1 (pp. 55-56). Pasadena, CA: NASA Jet Propulsion Laboratory.
10. Boardman, J. W., Kruse, F. A., & Green, R. O. (1995). Mapping target signatures via partial unmixing of AVIRIS data. Summaries of the fifth JPL Airborne Geoscience Workshop, JPL Publication 95-1 (pp. 23-26). Pasadena, CA: NASA Jet Propulsion Laboratory.
11. Bowles, J., Antoniades, M., Baumback, J.M. etal., Realtime analysis of hyperspectral data sets using NRL's ORASIS algorithm. SPIE Vol. 3118. 38,1997.
12. Calvin W M, Vaughan R G, Taranik J, etal.,2000. Spectral analysis of SEBASS data. Atmosphere correction, calibration surface emissivity and mineral mapping, Proceedings of the 14th International Conference on Applied Geologic Remote Sensing, 1, pp. 20-26.
13. Clark C. and Canas A., 1995, Spectral identification by artificial neural network and genetic algorithm, Int. J. Remote Sensing, Vol. 16 No. 12, 2255-2275.
14. Clark, R. N. et al., 1990, "High spectral resolution reflectance spectroscopy of minerals.", Journal of Geophysical Research, Vol. 95, No. B8,12653-12680, Aug. 10,1990.
15. Clark, R, N., and Roush, T. L. (1984), Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications, J. Geophys. Res. 89(B7):6329-6340.
16. Clark, R. N., and Swayze, G. A., 1995, Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice, and snow, and other materials: The USGS Tricorder Algorithm. In Summaries of the Fifth Annual JPL Airborne EartbScience Workshop, JPL Publication 95-1, v. 1, pp. 39-40.
17. Clark, R. N., Swayze, G. A., Gallagher, A., Gorelick, N., and Kruse, F. A., 1991, Mapping with imaging spectrometer data using the complete band shape Least squares algorithm simultaneously fit to multiple spectral features from multiple materials. In Proceedings of the Third Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, JPL Publication 91-28, pp. 2-3.
18. Clark, R. N., Swayze, G. A., and Gallagher, A., 1992, Mapping the mineralogy and lithologyof Canyonlands, Utah with imaging spectrometer data and the multiple spectral feature mapping algorithm. In Summaries of the Third Annual JPL Airborne Geoscience Workshop, JPL Publication 92-14, v 1, pp. 11-13.
19. Clark, R. N., Swayze, G. A., Gallagher, A. J., King, T. V. V., and Calvin, W. M., 1993, The U. S. Geological Survey, Digital Spectral Library. Version 1:0.2 to 3.0 microns. U.S. Geological Survey Open File Report 93-592, 1340 pages.
20. Cloutis, E.A., Connery, D.R., Dover, F.J., 1999. Agricultural crop monitoring using airborne multispectral imagery and C_band synthetic aperture radar, International Journal of Remote Sensing 20(4) pp. 767-787.
21. Craig, Maurice, D., 1994, "Minimum Volume Transforms for Remotely Sensed Data", IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, pp. 542-552. N. Keshava, J.F. Mustard, Spectral unmixing, IEEE Signal Processing Magazine 19 (1)(2002)44-57.
22. Farrand, W.,Harsanyi, J.C. Mapping the distribution of mine tailing in the coeur d'Alene river valley, Idaho, through the use of constrained energy minimization technique, Remote Sensing Environ. 59 (1997) 64-76.
23. Friedl, M.A., C.E. Brodley and A.H. Strahler, 1999, "Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales," IEEE Trans. Geoscience & R.S. vol. 37, no. 2, pp. 969-982.
24. Goetz, A. F. H. and L. C., Rowan 1981, Geological remote sensing. Science. 211:781-791.
25. Goetz, A. F. H., Vane, G., Solomon, J. E., and Rock, B. N., 1985, Imaging spectrometry for earth remote sensing:Science, 228, 1147-1153.
26. Green, A. A., Berinan, M., Switzer, P., and Craig, M. D. (1988), A transformation for ordering multispcctral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 26:65-74.
27. Green R O, Eastwood M L, Sarture C M, etal., 1998. Imaging spectroscopy and the airborne visible/infrafed imaging spectrometer(AVIRIS), Remote sensing of Environment, 65, pp. 227-248.
28. Haralick, R.M., Shanmugam, K.,and It's hak, D. Texture features for image classification. IEEE Trans. Sys., Man and Cybernetics, SMC-3,610-621,1973.
29. Harsanyi, J.C, Chang, C.-I, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach", IEEE Transactions on geoscience and remote sensing, vol. 32, pp. 779-785, July 1994.
30. Harsanyi, J.C, Detection and classi"cation of subpixel spectral signatures in hyperspectral image sequences, Ph.D. Dissertation, Department of Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 1993.
31. Harsanyi, J.C, Farrand, W, Chang, C.-I. Detection of subpixel spectral signatures in hyperspectral image sequences, AnnualMeeting, Proceedings of American Society of Photogrammetry & Remote Sensing, Reno, 1994, pp. 236-247.
32. Hunt G R. Electromagnetic radiation: the communication lind in remote sensing. In:Remote Sensing in Geology. Siegal B, Gillespia A. Wiley New York, 1980, 702.
33. Jia X, Richards J A. Progressive Two-class Decision Classifier for Optimisation Class Discriminations[J]. Remote Sensing of Environment. 1998, 63(3):289-297.
34. Jia X, Richards J A. Segmented principal components transformation for efficient hyperspectral remote image display and classification. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1):538-542.
35. Kohonen, T., 1988, Self-Organization and Associative Memory, Springer-Verlag, New York.
36. Lillesand and Kiefer, 1994, Remote Sensing and Image Intrpretaion, 3rd Edition, John Wiley and Sons Inc., New York.
37. Palmadesso, P. J., Antoniades, J. A., Bowles, J., Baumback, M., "The Use of Filter Vectors and Fast Convex Sets for the Analysis of Hyperspectral Data", ISSSR, Australia, November 1995.
38. Plaza, A., Martinez, P., Perez, R. M., "Spatial/Spectral Endmember Extraction by Multidimensional Morphological Operations," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 9, Sept. 2002.
39. Plaza, A., Martinez, P., Gualtieri, J.A., Perez, R. M., "Spatial/Spectral Endmember Extraction from AVIRIS Hyperspectral Data Using Mathematical Morphology," Summaries of the Ⅺ JPL Airborne Earth Science Workshop, 2001a.
40. Plaza, A., Martinez, P., Gualtieri, J.A., Perez, R.M., "Automated identification of endmembers from hyperspectral data using mathematical morphology," Proc. SPIE Image and Signal Processing for Remote Sensing Ⅶ, Toulouse, France, 2001b.
41. Roberts, D. A., Gardner, R., Church, R., Ustin, S., Scheer, G., Green, R.O., "Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models," Remote Sensing of Environment, vol. 65, pp. 267-279, 1998.
42. Ryherd, S., and Woodcock, C., 1996. Combining Spectral and textural data in the segmentation of remotely sensed images. Photo. Engi. Remote Sensing, 62(2): 181-194.
43. Sali, E., and Wolfson, H. Texture classification in aerial photographs and satellite data. Int. J. Remote Sensing, Vol. 13,3395-3408,1992.
44. Smailbegovie, Taranik, J V, Kruse F A, 2000. Fifteen years of hyperspeetral remote sensing over Virginia City, Nevada, Proceedings of the 14th International Conference on Applied Geologic Remote Sensing, 1, pp.1-10.
45. Smith, M. O., Roberts, D. A., Hill, J., et ah (1994) A new approach to determining spectral abundances of mixtures in multispeetral images. IEEE Trans. Geosci.
2.童庆禧,1995,电子光学遥感系统的过去、现在和未来,成象光谱技术发展初探,遥感科学新进展,科学出版社,pp51-60。
3.童庆禧,郑兰芬,1999,高光谱遥感发展现状,遥感知识创新文集。中国科学技术出版社,pp13-25。
4.刘建贵.高光谱城市地物及人工目标识别与提取.中国科学院博士论文.1999.
5. Bateson, C.A. and Curtiss, B. A tool for manual endmember selection and spectral Unfixing. in Summaries of the V JPL Airborne Earth Science Workshop, Pasadena, CA, 1993.
6. Bateson, C.A., Asner, G. P., Wessman, C.A., "Endmember Bundles: A New Approach to Incorporating Endmember Variability into Spectral Mixture Analysis," IEEE Transactions on Geoscience and Remote Sensing, vol. 38 issue 2 part: 2, pp. 1083-1094, March 2000.
7. Boardman, J. W., 1989, Inversion of imaging spectrometry data using singular value decomposition: in Proceedings, IGARSS'89, 12th Canadian Symposium on Remote Sensing, 4, pp. 2069-2072.
8. Boardman, J. W., 1993, Automated spectral unmixing of AVIRIS data using convex geometry concepts: in Summaries, Fourth JPL Airborne Geoscience Workshop, JPL Publication 93-26, v. 1, p. 11-14.
9. Boardman, J. W. (1998). Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering. Summaries of the seventh JPL Airborne Geoscience Workshop, JPL Publication 97-1 (pp. 55-56). Pasadena, CA: NASA Jet Propulsion Laboratory.
10. Boardman, J. W., Kruse, F. A., & Green, R. O. (1995). Mapping target signatures via partial unmixing of AVIRIS data. Summaries of the fifth JPL Airborne Geoscience Workshop, JPL Publication 95-1 (pp. 23-26). Pasadena, CA: NASA Jet Propulsion Laboratory.
11. Bowles, J., Antoniades, M., Baumback, J.M. etal., Realtime analysis of hyperspectral data sets using NRL's ORASIS algorithm. SPIE Vol. 3118. 38,1997.
12. Calvin W M, Vaughan R G, Taranik J, etal.,2000. Spectral analysis of SEBASS data. Atmosphere correction, calibration surface emissivity and mineral mapping, Proceedings of the 14th International Conference on Applied Geologic Remote Sensing, 1, pp. 20-26.
13. Clark C. and Canas A., 1995, Spectral identification by artificial neural network and genetic algorithm, Int. J. Remote Sensing, Vol. 16 No. 12, 2255-2275.
14. Clark, R. N. et al., 1990, "High spectral resolution reflectance spectroscopy of minerals.", Journal of Geophysical Research, Vol. 95, No. B8,12653-12680, Aug. 10,1990.
15. Clark, R, N., and Roush, T. L. (1984), Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications, J. Geophys. Res. 89(B7):6329-6340.
16. Clark, R. N., and Swayze, G. A., 1995, Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice, and snow, and other materials: The USGS Tricorder Algorithm. In Summaries of the Fifth Annual JPL Airborne EartbScience Workshop, JPL Publication 95-1, v. 1, pp. 39-40.
17. Clark, R. N., Swayze, G. A., Gallagher, A., Gorelick, N., and Kruse, F. A., 1991, Mapping with imaging spectrometer data using the complete band shape Least squares algorithm simultaneously fit to multiple spectral features from multiple materials. In Proceedings of the Third Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, JPL Publication 91-28, pp. 2-3.
18. Clark, R. N., Swayze, G. A., and Gallagher, A., 1992, Mapping the mineralogy and lithologyof Canyonlands, Utah with imaging spectrometer data and the multiple spectral feature mapping algorithm. In Summaries of the Third Annual JPL Airborne Geoscience Workshop, JPL Publication 92-14, v 1, pp. 11-13.
19. Clark, R. N., Swayze, G. A., Gallagher, A. J., King, T. V. V., and Calvin, W. M., 1993, The U. S. Geological Survey, Digital Spectral Library. Version 1:0.2 to 3.0 microns. U.S. Geological Survey Open File Report 93-592, 1340 pages.
20. Cloutis, E.A., Connery, D.R., Dover, F.J., 1999. Agricultural crop monitoring using airborne multispectral imagery and C_band synthetic aperture radar, International Journal of Remote Sensing 20(4) pp. 767-787.
21. Craig, Maurice, D., 1994, "Minimum Volume Transforms for Remotely Sensed Data", IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, pp. 542-552. N. Keshava, J.F. Mustard, Spectral unmixing, IEEE Signal Processing Magazine 19 (1)(2002)44-57.
22. Farrand, W.,Harsanyi, J.C. Mapping the distribution of mine tailing in the coeur d'Alene river valley, Idaho, through the use of constrained energy minimization technique, Remote Sensing Environ. 59 (1997) 64-76.
23. Friedl, M.A., C.E. Brodley and A.H. Strahler, 1999, "Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales," IEEE Trans. Geoscience & R.S. vol. 37, no. 2, pp. 969-982.
24. Goetz, A. F. H. and L. C., Rowan 1981, Geological remote sensing. Science. 211:781-791.
25. Goetz, A. F. H., Vane, G., Solomon, J. E., and Rock, B. N., 1985, Imaging spectrometry for earth remote sensing:Science, 228, 1147-1153.
26. Green, A. A., Berinan, M., Switzer, P., and Craig, M. D. (1988), A transformation for ordering multispcctral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 26:65-74.
27. Green R O, Eastwood M L, Sarture C M, etal., 1998. Imaging spectroscopy and the airborne visible/infrafed imaging spectrometer(AVIRIS), Remote sensing of Environment, 65, pp. 227-248.
28. Haralick, R.M., Shanmugam, K.,and It's hak, D. Texture features for image classification. IEEE Trans. Sys., Man and Cybernetics, SMC-3,610-621,1973.
29. Harsanyi, J.C, Chang, C.-I, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach", IEEE Transactions on geoscience and remote sensing, vol. 32, pp. 779-785, July 1994.
30. Harsanyi, J.C, Detection and classi"cation of subpixel spectral signatures in hyperspectral image sequences, Ph.D. Dissertation, Department of Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 1993.
31. Harsanyi, J.C, Farrand, W, Chang, C.-I. Detection of subpixel spectral signatures in hyperspectral image sequences, AnnualMeeting, Proceedings of American Society of Photogrammetry & Remote Sensing, Reno, 1994, pp. 236-247.
32. Hunt G R. Electromagnetic radiation: the communication lind in remote sensing. In:Remote Sensing in Geology. Siegal B, Gillespia A. Wiley New York, 1980, 702.
33. Jia X, Richards J A. Progressive Two-class Decision Classifier for Optimisation Class Discriminations[J]. Remote Sensing of Environment. 1998, 63(3):289-297.
34. Jia X, Richards J A. Segmented principal components transformation for efficient hyperspectral remote image display and classification. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1):538-542.
35. Kohonen, T., 1988, Self-Organization and Associative Memory, Springer-Verlag, New York.
36. Lillesand and Kiefer, 1994, Remote Sensing and Image Intrpretaion, 3rd Edition, John Wiley and Sons Inc., New York.
37. Palmadesso, P. J., Antoniades, J. A., Bowles, J., Baumback, M., "The Use of Filter Vectors and Fast Convex Sets for the Analysis of Hyperspectral Data", ISSSR, Australia, November 1995.
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