An Approach to Classify Tall Vegetation and Urban Using Deoriented PALSAR Image

Abstract

Researchers are attempting to classify the fully polarimetric synthetic aperture radar data utilizing diverse methods based on polarimetric indices, decomposition, and image analysis. Although, all these techniques have great potential, and able to segregate distinct land cover classes, but still there occurs ambiguity in classifying urban and tall vegetation classes as they both show similar kind of double-bounce scattering characteristics. In the past, Yamaguchi has modified the polarization process by applying the deorientation effect, which has enhanced the double-bounce characteristic of similar classes like urban and tall vegetation. Based on this, an attempt has been made in this letter to remove the uncertainty between the two classes by proposing a deorientation feature-based classification algorithm which could segregate urban and tall vegetation in an unsupervised way. In this, along with polarimetric, other features, namely, color, texture, and wavelets, have been critically analyzed on the deoriented image as each feature type has its own points of interest and hindrances. The result obtained from the proposed technique has shown good accuracy rate for urban and tall vegetation classification.