Unsupervised land cover classification of SAR images by contour tracing

Abstract

The potentiality of synthetic aperture radar (SAR) images for land cover mapping is an important area of research. For single band, single polarized SAR images, information is available in the form of intensity and texture only. Land cover classification of SAR images requires exploitation of spatial relationship of pixels also, in addition to pixel level segmentation. SAR images can be segmented successfully if the regions with homogeneous intensity and texture areas can be identified and grouped together. So far, contour tracing has been used only in demarcating sea and land. Identifying contours in a domesticated area with a mixture of water, urban and vegetation areas require complex analysis of the spatial distribution of pixels. In this paper, we have presented an unsupervised classification algorithm using maximum a posteriori (MAP) segmentation for SAR images in which SAR image is classified into monotone, texture and edge regions. Monotone and textured regions are labeled as land cover types like water, urban and vegetation areas using K-means classification. SAR image of the region with latitude varying from 77.86deg to 77.91deg and longitude varying between 29.89deg and 29.85deg of Haridwar region, India is considered for segmentation. We have compared the segmented image obtained by this methodology with the topographic map of the corresponding region. The water, urban and vegetation areas are clearly recognized with the proposed classification approach which represents a very good agreement with the original topographic sheet.