| Technology and Methodology |
|
|
|
|
|
Classification model for "same subject with different spectra" on complicated surface in Southern hilly areas |
| YANG Yuhui, YAN Meichun, LI Zhijia, YU Qing, CHEN Beibei |
| Department of GIS, School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China |
|
|
|
|
Abstract Mixed ground objects in complex basin are easily interfered by background, and foreground mixed in different ground objects is difficult to be distinguished by one rule. In this paper, the authors discuss the classification rule model of common ground feature in different mixed backgrounds. With Landsat8 images as the data source, level 1 tributary of Zhengshui River basin in Xiangjiang River basin and the three big cities of the Yangtze River delta as the study areas, the authors adopted the maximum likelihood method to conduct a preliminary classification. Based on analyzing the spectral characteristics of mixed feature, the authors built the classification decision tree of mixed ground feature to identify water, artificial construction, farmland, bare land, forest land and bare rock. The results obtained by the authors show that the overall accuracy of the Zhengshui River is about 88.21%, which is higher than the supervised classification accuracy of 79.68%, and the overall accuracy of other three cities along the Yangtze River is higher than 92%. It is shown that the classification model for mixed subjects can improve the accuracy of the same ground objects with different backgrounds.
|
| Keywords
remote sensing
mining subsidence
hazard
coal mine
|
|
|
|
Issue Date: 14 April 2016
|
|
|
| [1] 周国琼.面向对象的TM影像分类[D].昆明:昆明理工大学,2012. Zhou G Q.Landsat TM Imagery Classification Using Object-oriented Technology[D].Kunming:Kunming University of Science and Technology,2012.[2] 李彤,吴骅.采用决策树分类技术对北京市土地覆盖现状进行研究[J].遥感技术与应用,2004,19(6):485-487. Li T,Wu H.Application of decision tree classification to Peking land cover[J].Remote Sensing Technology and Application,2004,19(6):485-487.[3] 余坤勇,刘健,许章华,等.南方地区竹资源专题信息提取研究[J].遥感技术与应用,2009,24(4):449-455. Yu K Y,Lu J,Xu Z H,et al.Study on bamboo resources thematic information extraction in the south of China[J].Remote Sensing Technology and Application,2009,24(4):449-455.[4] 宫鹏,黎夏,徐冰.高分辨率影像解译理论与应用方法中的一些研究问题[J].遥感学报,2006,10(1):1-5. Gong P,Li X,Xu B.Interpretation theory and application method development for information extraction from high resolution remotely sensed data[J].Journal of Remote Sensing,2006,10(1):1-5.[5] Joy S M,Reich R M,Reynolds R T.A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees[J].International Journal of Remote Sensing,2003,24(9):1835-1852.[6] 胥海威,何宽.改进随机决策树群算法在监督分类中的应用[J].地理与地理信息科学,2010,26(6):38-40. Xu H W,He K.An improved random decision trees algorithm with application to supervised classification[J].Geography and Geo-information Science,2010,26(6):38-40.[7] Haralick R M.Statistical and structural approaches to texture[J].Proceedings of the IEEE,1979,67(5):786-804.[8] 陈贝贝,颜梅春,李致家,等.分类器和时相结合的流域下垫面分类方法[J].测绘科学,2014,39(9):141-144. Chen B B,Yan M C,Li Z J,et al.Basin underlying surface classification based on classifiers and phase combined[J].Science of Surveying and Mapping,2014,39(9):141-144.[9] 颜梅春.高分辨率影像的植被分类方法对比研究[J].遥感学报,2007,11(2):235-240. Yan M C.Research and contrast on several vegetation-classification methods of high-resolution satellite image data[J].Journal of Remote Sensing,2007,11(2):235-240.[10] 袁林山,杜培军,张华鹏,等.基于决策树的CBERS遥感影像分类及分析评价[J].国土资源遥感,2006,18(2):92-98.doi:10.6046/gtzyyg.2008.02.22. Yuan L S,Du P J,Zhang H P,et al.CBERS imagery classification based on decision tree and derformance analysis[J].Remote Sensing for Land and Resources,2006,18(2):92-98.doi:10.6046/gtzyyg.2008.02.22. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
2016,
Vol. 28
