Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network

NCUIR > Research Center > Center for Space and Remote Sensing Research > journal & Dissertation > Item 987654321/27685

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27685

Title:	Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network
Authors:	Chen,KS;Huang,WP;Tsay,DH;Amar,F
Contributors:	太空及遙測研究中心
Keywords:	SPECKLE REDUCTION
Date:	1996
Issue Date:	2010-06-29 18:52:11 (UTC+8)
Publisher:	中央大學
Abstract:	A practical method for extracting microwave backscatter for terrain-cover classification is presented in this paper. The test data are multifrequency (P, L, C bands) polarimetric SBR data acquired by JPL over an agricultural area called ''Flevoland.'' The terrain covers include forest, water, bare soil, grass, and eight other types of crops. The radar response of crop types to frequency and polarization states were analyzed for classification based on three configurations: 1) multifrequency and single-polarization images; 2) single-frequency and multipolarization images; and 3) multifrequency and multipolarization images. A recently developed dynamic learning neural network was adopted as the classifier. Results show that using partial information, P-band multipolarization images and multiband hh polarization images, have better classification accuracy, while with a full configuration, namely, multiband and multipolarization, gives the best discrimination capability. The overall accuracy using the proposed method can be as high as 95% with a total of thirteen cover types classified. Further reduction of the data volume by means of correlation analysis was conducted to single out the minimum data channels required. It was found that this method efficiently reduces the data volume while retaining highly acceptable classification accuracy.
Relation:	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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