NDLI: Land cover classification by Support Vector Machines using multi-temporal polarimetric SAR data

Content Provider	IEEE Xplore Digital Library
Author	Qi Feng Er-xue Chen Zengyuan Li Ying Guo Wei Zhou Weimei Li Guangcai Xu
Copyright Year	2012
Description	Author affiliation: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China (Qi Feng; Er-xue Chen; Zengyuan Li; Ying Guo; Wei Zhou; Weimei Li; Guangcai Xu)
Abstract	In order to improve the land cover classification accuracy for SAR image, Support Vector Machine (SVM), which has wide applicability is used on the land cover classification of POLSAR image in this paper. The study site is located in Tahe County, Heilongjiang Province, China, and two scenes of quad-polarization Radarsat-2 SAR images were acquired. the land cover classification of single-temporal POLSAR image by SVM, and multi-temporal POLSAR image by SVM and maximum likelihood classification (MLC) is studied separately. Then all the classification results are evaluated. Some conclusions can be got according to the analysis of all results and accuracy: Firstly, it is difficult to distinguish the different types of vegetation for the similar scattering among them in July. However, water, whose scattering characteristic is simplex, can be distinguished from others easily. Scondly, in October, the scattering characteristics among forest, shrub, grass, crop are different, therefore it is easy to distinguish vegetation because of their one from others in this period. But for water, with reduced in winter, the river width narrows, compared with it in summer, water classification accuracy is lower in this period. Thirdly, joint July and October SAR data for classification, can offset espective their own disadvantages. and improve overall accuracy. And the last one, With the characteristics that different probability density distribution, small sample, non-linear and so on, SVM shows the wide applicability.
Starting Page	6244
Ending Page	6246
File Size	345763
Page Count	3
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6352685
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Synthetic aperture radar Remote sensing Accuracy Vegetation mapping Scattering Feature extraction land cover POLSAR multi-temporal SVM
Content Type	Text
Resource Type	Article

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