The Use of a Modified GOPCE Method for Forest and Nonforest Discrimination

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.