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REMOTE SENSING FOR LAND & RESOURCES    2011, Vol. 23 Issue (3) : 82-87     DOI: 10.6046/gtzyyg.2011.03.15
Technology Application |
Automatic Interpretation of High Resolution Remotely Sensed Images by Using Kernel Method
ZHANG Wei1, ZHANG Wei2, LIU Shi-ying3, YANG Jin-zhong1, MAO Sheng-yi4
1. China Aero Geophysical Survey & Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. Institute of Geological Survey of Sichuan Province, Chengdu 610081, China;
3. Institute of Geological Survey of Qinghai Province, Xining 810012, China;
4. Guangzhou Institute of Geochemistry, Chinese Academy of Science, Guangzhou 510640, China
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Abstract  

To tackle the limitation of conventional pixel-based classification methods, this paper proposes a new approach composed of three steps, namely kernel principal component analysis (KPCA) based feature extraction, support vector machine (SVM) classification and majority filtering post-classification. An experiment with an IKONOS image covering a study area in Tibet indicates the effectiveness of this approach. The resultant image from this automatic method shows a pattern very similar to the pattern of the reference map interpreted manually.

Keywords Simple polygon      Identifying convexity-concavity      Vector-product method     
: 

TP 751.1

 
Issue Date: 07 September 2011
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SONG Xiao-mei
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ZHOU Cheng-hu
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SONG Xiao-mei,CHENG Chang-xiu,ZHOU Cheng-hu. Automatic Interpretation of High Resolution Remotely Sensed Images by Using Kernel Method[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(3): 82-87.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.03.15     OR     https://www.gtzyyg.com/EN/Y2011/V23/I3/82


[1] 朱亮璞.遥感地质学
[M].北京:地质出版社,1994:168-176.

[2] Gupta R P.Remote Sensing Geology,2nd Edition
[M].Berlin:Springer-Verlag,Heidelberg,New York,2003:655-661.

[3] Wlkerr T.A Remote Sensing Study of Active Folding and Faulting in Southern Kerman province,S E Iran
[J].Journal of Structural Geology,2006(28):654-668.

[4] Metternicht G,Hurni L,Gogu R.Remote Sensing of Landslides:An Analysis of the Potential Contribution to Geo-spatial Systems for Hazard Assessment in Mountainous Environments
[J].Remote Sensing of Environment,2005,98(2/3):284-303.

[5] 王瑜玲,刘少峰,李婧,等.基于高分辨率卫星遥感数据的稀土矿开采状况及地质灾害调查研究
[J].江西有色金属,2006,20(1):10-14.

[6] Lin A,Nishikawa M.Coseismic Lateral Offsets of Surface Rupture Zone Produced by the 2001 M-w 7.8 Kunlun Earthquake,Tibet from the IKONOS and QuickBird Imagery
[J].International Journal of Remote Sensing,2007,28(11):2431-2445.

[7] 张自力,秦其明,曹宝,等.高分辨率遥感影像在岩墙地质体信息提取中的应用
[J].地理与地理信息科学,2007,23(3):15-18.

[8] 满旺.高分辨率遥感铀矿地质勘查技术体系研究
[J].厦门理工学院学报,2009,17(3):33-36.

[9] 王治华,徐起徳,徐斌.岩门村滑坡高分辨率遥感调查与机制分析
[J].岩石力学与工程学报,2009,28(9):1810-1818.

[10] 仇江啸,王效科.基于高分辨率遥感影像的面向对象城市土地覆被分类比较研究
[J].遥感技术与应用,2010,25(5):653-661.

[11] Laliberte A S,Rango A,Havstad K M,et al.Object-oriented Image Analysis for Mapping Shrub Encroachment from 1937 to 2003 in Southern New Mexico
[J].Remote Sensing of Environment,2004,93(1/2):198-210.

[12] 陈杰,邓敏,肖鹏峰,等.基于分水岭变换与空间聚类的高分辨率遥感影像面向对象分类
[J].遥感技术与应用,2010,25(5):597-603.

[13] Baatz M,Shape A.Multiresolution Segmentation:An Optimization Approach for High Quality Multi-scale Image Segmentation
[C]//Strobl J,Baschke T,Griesebner G.Angewandte Geographische Informationsverarbeitung XII,Wichmann-Verlag:Heidelberg,2000:12-23.

[14] 章孝灿,黄智才,赵元洪.遥感数字图像处理
[M].杭州:浙江大学出版社,1997.

[15] Zhao G,Maclean A L.A Comparison of Canonical Discriminant Analysis and Principal Component Analysis for Spectral Transformation
[J].Phtotogrammetric Engineering & Remote Senisng,2000,66(7):841-847.

[16] Mitternicht G I,Zinck J A.Remote Sensing of Soil Salinity: Potentials and Constraints
[J].Remote Sensing of Environment,2003,85(1):1-20.

[17] Crosta A P,Sabine C,Taranik J V.Hydrothermal Alteration Mapping at Bodie,California,Using AVIRIS Hyperspectral Data
[J].Remote Sensing of Environment,1998,65(1):309-319.

[18] Ruiz-Armenta J R,Prol-Ledesma R M.Techniques for Enhancing the Spectral Response of Hydrothermal Alteration Minerals in Thematic Mapper Images of Central Mexico
[J].International Journal of Remote Sensing,1998,19(10):1981-2000.

[19] Tangestani M H,Moore F.Iron Oxide and Hydroxyl Enhancement Using the Crosta Method:A Case Study from the Zagros Belt,Fars Province,Iran
[J].International Journal of Applied Earth Observation and Geoinformation,2000,2(1):140-146.

[20] Carranza E J M,Hale M.Mineral Imaging with Landsat Thematic Mapper Data for Hydrothermal Alteration Mapping in Heavily Vegetated Terrane
[J].International Journal of Remote Sensing,2002,23(22):4827-4852.

[21] Almeida-Filho R.Remote Detection of Hydrocarbon Microseepage Areas in the Serra do Tona Region,Tucano Basin,Brazil
[J].Canadian Journal of Remote Sensing,2002,28(2):750-757.

[22] Huang C,Davis L S,Townshend J R G.An Assessment of Support Vector Machines for Land Cover Classification
[J].International Journal of Remote Sensing,2002,23(4):725-749.

[23] Zhu G,Blumberg D G.Classification Using ASTER Data and SVM Algorithms:The Case Study of Beer Sheva,Israel
[J].Remote Sensing of Environment,2002,80:233-240.

[24] Foody G M,Mathur A.A Rrelative Evaluation of Multiclass Iimage Classification by Support Vector Machines
[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(6):1335-1343.

[25] Pal M,Mather P M.Support Vector Classification in Rremote Sensing
[J].International Journal of Remote Sensing,2005,26(6):1007-1011.

[26] Oommen T,Misra D,Twarakavi N K C,et al.An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing
[J].Mathematical Geosciences,2008,40:409-422.

[27] Yu L,Porwal A,Holden E J,et al.Towards Automatic Lithological Classification from Remote Sensing Data Using Support Vector Machines
[C]//In,EGU2010.Vienna,Austria:2010.

[28] 汤国安,张友顺,刘咏梅.遥感数字图像处理
[M].北京:科学出版社,2004:274.

[29] 王冬梅,吴卿,王西林,等.应用高分辨率卫星影像提取水土保持措施信息的分类后处理技术研究
[J].中国水土保持,2006(5):42-43.

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