TEXTURE ANALYSIS FOR CLASSIFICATION OF RISAT-II IMAGES

Abstract

RISAT-II or Radar Imaging satellite – II is a microwave-imaging satellite lunched by ISRO to take images of the earth during day and night as well as all weather condition. This satellite enhances the ISRO's capability for disaster management application together with forestry, agricultural, urban and oceanographic applications. The conventional pixel based classification technique cannot classify these type of images since it do not take into account the texture information of the image. This paper presents a method to classify the high-resolution RISAT-II microwave images based on texture analysis. It suppress the speckle noise from the microwave image before analysis the texture of the image since speckle is essentially a form of noise, which degrades the quality of an image; make interpretation (visual or digital) more difficult. A local adaptive median filter is developed that uses local statistics to detect the speckle noise of microwave image and to replace it with a local median value. Local Binary Pattern (LBP) operator is proposed to measure the texture around each pixel of the speckle suppressed microwave image. It considers a series of circles (2D) centered on the pixel with incremental radius values and the intersected pixels on the perimeter of the circles of radius r (where r = 1, 3 and 5) are used for measuring the LBP of the center pixel. The significance of LBP is that it measure the texture around each pixel of the image and computationally simple. ISODATA method is used to cluster the transformed LBP image. The proposed method adequately classifies RISAT-II X band microwave images without human intervention.