Abstract
In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.
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References
CHEN Shu-peng, TONG Qing-xi, GUO Hua-dong, 1999. Remote Sensing Information Mechanism[M]. Beijing: Science Press, 242–245. (in Chinese)
DAI Chang-da, LEI Li-ping, 1989. The spectral information content feature and best band combination of TM image [J]. Remote Sensing of Environment, 4(4): 282–292. (in Chinese)
FANG Hong-liang, HUANG Xuan, 1997. Remote sensing technique applied in Geoscience-a review of its present development [J]. Geographical Research, 16(2): 96–103. (in Chinese)
FANG Sheng-hui, SHU Ning, WU Zhao-cong, 1998. The study of noise reduction for SAR image[J]. Journal of Wuhan Technical University of Surveying and Mapping, 23(3): 215–218. (in Chinese)
HINTON J C, 1996. GIS and remote sensing integration for environmental applications [J]. Int. J. Geographical Information Systems, 10 (7): 877–890.
LI Chun-sheng, YAN Ying, CHEN Jie et al., 2000. Speckle reduction for high resolution one-look space borne SAR images[J]. Acta Electronica Sinica, 28(3): 13–16. (in Chinese)
LI De-ren, GUAN Ze-qun, 2000. Integration and Realization of Spatial Information System[M]. Wuhan: Press of Wuhan Technical University of Surveying and Mapping. (in Chinese)
TANG Ling-li, JIANG Ping, DAI Chang-da et al., 1996. Comparison and studies on speckle noise reduction of satellite SAR image [A]. In: PAN Xi-zhe (ed.) Spaceborne SAR Image Processing[C]. Beijing: Science Press, 83–88. (in Chinese)
WAKABAYASHI H, 1996. A method of speckle noise reduction for SAR data [J]. J. of Remote Sensing, 17(10): 380–389.
XU Xin, LIAO Ming-sheng, ZHU Pan et al., 1999. Research for speckle filtering of single-look SAR image[J]. Journal of Wuhan Technical University of Surveying and Mapping, 24(4): 312–315. (in Chinese)
ZHENG Hong, PAN Li, 1999. The automatic selection of image threshold on the basis of Genetic Algorith[J]. Journal of Image and Graphics, 4A(4): 327–330. (in Chinese)
ZHOU Cheng-hu, LUO Jian-cheng, et al., 1999. Geo-Understanding and Analysis of RS image[M]. Beijing: Science Press. (in Chinese).
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Foundation item: Under the auspices of the Research Foundation of Doctoral Point of China(No. RFDP20010290006).
Biography: DU Pei-jun(1975 — ), male, a native of Wutai County, Shaanxi Province, Ph. D., associate professor, specialized in theories and application of RS, GIS and their integration.
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Du, Pj., Zhou, Xd. & Guo, Dz. Some key issues on the application of satellite remote sensing to mining areas. Chin. Geograph.Sc. 13, 79–83 (2003). https://doi.org/10.1007/s11769-003-0089-1
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DOI: https://doi.org/10.1007/s11769-003-0089-1