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高分辨率遙感圖像的聚類

鄧湘金 王彥平 彭海良

鄧湘金, 王彥平, 彭海良. 高分辨率遙感圖像的聚類[J]. 電子與信息學(xué)報(bào), 2003, 25(8): 1073-1080.
引用本文: 鄧湘金, 王彥平, 彭海良. 高分辨率遙感圖像的聚類[J]. 電子與信息學(xué)報(bào), 2003, 25(8): 1073-1080.
Deng Xiangjin, Wang Yanping, Peng Hailiang. The clustering of high resolution remote sensing imagery[J]. Journal of Electronics & Information Technology, 2003, 25(8): 1073-1080.
Citation: Deng Xiangjin, Wang Yanping, Peng Hailiang. The clustering of high resolution remote sensing imagery[J]. Journal of Electronics & Information Technology, 2003, 25(8): 1073-1080.

高分辨率遙感圖像的聚類

The clustering of high resolution remote sensing imagery

  • 摘要: 高分辨率遙感圖像中的細(xì)小目標(biāo)(如道路等)使圖像的同類區(qū)域表現(xiàn)變得不一致,從而增加了高分辨率圖像聚類的難度,本文提出了一種高分辨率遙感圖像的聚類方法,其聚類過程包括如下三個(gè)步驟:第一步,在滑動(dòng)窗口內(nèi)使用消除次要成分法處理遙感圖像,該處理過程使用一維形態(tài)學(xué)分水嶺技術(shù)獲得直方圖中的左側(cè)閾值和右側(cè)閾值,再根據(jù)這兩個(gè)閾值濾除圖像中的次要成分;第二步,計(jì)算滑動(dòng)窗口內(nèi)的圖像特征;第三步根據(jù)圖像特征量利用BPC(Back Propagation and Competitive)網(wǎng)絡(luò)進(jìn)行圖像聚類。三組試驗(yàn)(本文提出的聚類算法,最鄰近距離聚類法,K均值聚類法)表明本文提出的圖像聚類方法可以有效實(shí)現(xiàn)高分辨率遙感圖像的聚類。
  • 駱劍承.周成虎,楊艷,人工神經(jīng)網(wǎng)絡(luò)遙感影像分類模型及其與知識(shí)集成方法研究,遙感學(xué)報(bào),2001,5(2),122-129.[2]A. Banerjee, P. Burlina, F. Alajaji, Image segmentation and labeling using the Polya Urn model,IEEE Trans. on Image Processing, 1999, 8(9), 1243-1253.[3]G. Kuntimad, H. S. Ranganath, Perfect image segmentation using pulse coupled neural networks,IEEE Trans. on Neural Networks, 1999, 10(3), 591-598.[4]Y.A. Tolias, S. M. Panas, Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions, IEEE Trans. on Syst., Man, and Cybernetics, part A: Syst. and Humans, 1998, 28(3), 359-369.[5]Y. Dong, A. K. Milne, B. C. Forster, Segmentation and classification of vegetated areas using polarimetric SAR image data, IEEE Trans. on Geoscience and Remote Sensing, 2001, 39(2),321-329.[6]S.R. Seethalakshmy, P. Srivastava, J. Majumdar, Multi-modal image segmentation using a modified Hopfield neural network, Pattern Recognition, 1998, 31(6), 743-750.[7]J.E. Koss, F. D. Newman, T. K. Johnson, D. L. Kirch, Abdominal organ segmentation using texture transforms and a Hopfield neural network, IEEE Trans. on Medical Imaging, 1999, 18(7),640-648.[8]T. Nelson, K. O. Niemann, M. Wulder, Spatial statistical techniques for aggregating point objects extracted from high spatial resolution imagery, IEEE IGARSS(International Geoscience and Remote Sensing Symposium), 2001, (from the CD-ROM of 2001 IEEE IGARSS).Q. Zhang, J. Wang, P. Gong, P. Shi, Texture analysis for urban spatial pattern study usingSPOT imagery, IEEE IGARSS(International Geoscience And Remote Sensing Symposium), 2001. (from the CD-ROM of 2001 IEEE IGARSS)[9]J. A. Bendiktsson, M. Pesaresi, Feature extracted and classification of urban high-resolution satellite imagery based on morphological preprocessing, IEEE IGARSS(International Geoscience And Remote Sensing Symposium), 2001, (from the CD-ROM of 2001 IEEE IGARSS).[10]P. Soille, Morphological Image Analysis: Principles and Applications, New York, Springer-Verlag,1999, 1-158.[11]Cris L. Luengo Hendriks, Marjolein van der Glas, Lucas J. van Vliet, Image Analysis, CSP:Computer Service Labs, Delft University, Holland, November 6, 2001, 28-30.[12]Chris Oliver, Shaun Quegan, Understanding Synthetic Aperture Radar Images, Boston/London,Artech House, Inc., 1998, 195-295.[13]M.B. Charles, F. D. Timothy, D. Kevin, Automatic land-cover classification of a Barrier Island in the Virginia coast, IEEE IGARSS(International Geoscience And Remote Sensing Symposium),2001, (from the CD-ROM of 2001 IEEE IGARSS).[14]邊肇祺,等編著,模式識(shí)別,北京,清華大學(xué)出版社,1988,216-284[15]姚天任,孫洪著,現(xiàn)代數(shù)字信號(hào)處理,武漢,華中科技大學(xué)出版社,1999,183-201.[16]鄒謀炎,反卷積和信號(hào)復(fù)原,北京,國防工業(yè)出版社,1999,12-63.
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  • 收稿日期:  2002-01-24
  • 修回日期:  2002-06-17
  • 刊出日期:  2003-08-19

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