An investigation of the selection of texture features for crop discrimination using SAR imagery

Abstract

This article presents a methodology for selecting texture measures to maximize the discrimination of agricultural land use classes in SAR images. The images were acquired during the first flight of the Shuttle Imaging Radar-C (SIR-C) experiment, in April 1994. L (24 cm)- and C (5 cm)-band SAR data at HH (horizontal transmitting and receiving), HV (horizontal transmitting, vertical receiving), and VV (vertical transmitting and receiving) polarizations both in ground range and slant range and in two different passes were analyzed. The kappa statistic was used to identify meaningful texture measures to discriminate seven classes. The results show that the classifications of land use based only on tonal averages produced a kappa coefficient only slightly higher than 0.50. A kappa threshold of 0.90 was reached with the simultaneous inclusion of 15 texture measures for the six images (two bands, three polarizations). It was also found that the inclusion of texture features when only one band and one polarization was used could produce kappa values higher than 0.85.