NDLI: Unsupervised classification of forest from polarimetric interferometric SAR data using fuzzy clustering

Content Provider	IEEE Xplore Digital Library
Author	Huan-Min Luo Er-Xue Chen Xiao-Wen Li Jian Cheng Min Li
Copyright Year	2010
Description	Author affiliation: Institute of Geo-spatial Information Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China (Huan-Min Luo; Xiao-Wen Li; Min Li) \|\| School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China (Jian Cheng) \|\| Institute of Forest Resources Information Technique of Chinese Academy of Forestry, Beijing 100091, China (Er-Xue Chen)
Abstract	Fuzzy clustering algorithms have been successfully applied to POLSAR classification, but not to POLInSAR. In this paper, a Fuzzy C Means (FCM) clustering algorithm integrating the complementary physical information and statistical property contained in both polarimetric and interferometric data, is used for POLInSAR classification. At first, the area dominated by volume scattering is extracted from polarimetric information using unsupervised H-A-Alpha k-means Wishart classifier with the physical scattering mechanisms of different terrain types; and the volume scattering area (forest area) is further segmented in the feature space of the relative optimal interferomet-ric coherence spectrum A1 and A2. Then a robust unsupervised fuzzy C means (FCM) classifier initialized with the results of the segmentation is applied to the polarimetric interferometric coherency data sets corresponding to the volume scattering area. This will not only take into account the scattering mechanisms of the data, so that the results of the classification have definite physical meaning, but also avoid the problem that the initial value of FCM algorithm is difficult to identify. The proposed method is evaluated and compared with k-means Wishart classifier using repeat pass E-SAR L band polarimetric interfer-ometric SAR data and the corresponding auxiliary image. Preliminary results show that the proposed method has better performance.
Starting Page	201
Ending Page	206
File Size	566371
Page Count	6
File Format	PDF
ISBN	9781424465309
e-ISBN	9781424465316
DOI	10.1109/ICWAPR.2010.5576325
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	POLInSAR Fuzzy C means algorithm K-means algorithm Forest classification Clustering algorithms Optimized production technology Coherence Classification algorithms Biomass Pixel Scattering mechanism Relative optimal inter-ferometric coherence spectrum
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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