DocumentCode :
15962
Title :
The Use of a Modified GOPCE Method for Forest and Nonforest Discrimination
Author :
Junjun Yin ; Zheng-Shu Zhou ; Moon, Wooil M. ; Ruijin Jin ; Caccetta, Peter A.
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
Volume :
12
Issue :
5
fYear :
2015
fDate :
May-15
Firstpage :
1076
Lastpage :
1080
Abstract :
This study focuses on the development and evaluation of the generalized optimization of polarimetric contrast enhancement (GOPCE) model to discriminate between forested and nonforested areas. The main objective is to investigate the performance of the GOPCE method for forest mapping and to assess the potential of different polarimetric parameters for forest representation. We make two modifications to the original GOPCE method. First, by comparing behaviors of different polarimetric parameters, the GOPCE model is modified. Then, linear discriminant analysis is employed for further optimization of the target contrast. Forest/nonforest discrimination results are demonstrated on L-band fully polarimetric ALOS-1/PALSAR data acquired over a pilot study area in northeastern Tasmania, Australia, where the main forest type is eucalypt forests. Two other forest classification approaches (i.e., support vector machine and canonical variate analysis) are also tested for comparison. The final results obtained from the modified GOPCE model with the generalized Fisher criterion can improve the forest/nonforest discrimination accuracy.
Keywords :
geophysical image processing; image classification; image enhancement; image representation; radar polarimetry; remote sensing by radar; support vector machines; synthetic aperture radar; vegetation mapping; Australia; L-band fully polarimetric ALOS-1/PALSAR data; canonical variate analysis; eucalypt forests; forest classification; forest discrimination; forest mapping; forest representation; generalized optimization of polarimetric contrast enhancement; modified GOPCE method; nonforest discrimination; northeastern Tasmania; support vector machine; Accuracy; Optimization; Remote sensing; Scattering; Support vector machines; Synthetic aperture radar; Vectors; Fisher criterion; forest and nonforest discrimination; generalized optimization of polarimetric contrast enhancement (GOPCE); radar polarimetry;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2014.2381211
Filename :
7008471
Link To Document :
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