Hierarchical Feature-Based Classification Approach for Fast and User-Interactive SAR Image Interpretation

The framework of this paper is focused on semiautomatic fast recognition of areas of interest for fast and user-interactive synthetic aperture radar (SAR) image interpretation for which only a unique intensity SAR image is available. The goal is to label regions into classes significant to a given application in an image, as rapidly as possible. A semiautomated "rough" classification is proposed. It defines the information extraction as a two-level procedure. The technique is based on a first partition image into homogeneous regions using the approach proposed by Galland et al. Then, discrimination characteristics are determined in each homogeneous region. This allows one to automatically obtain a first segmentation of the image into semantic regions of interest. Finally, this segmentation can be easily modified by a user in a limited computational time. At this level, they are considered as "objects," to identify which typical class of ground it can be attached to. Among a large set of tested measures, we have selected the most pertinent ones for the considered SAR images. In fact, we will see that to obtain an accurate measures estimation, measures need to be estimated inside a neighborhood as homogeneously as possible. This can be achieved with a reasonable confidence in the proposed approach due to the homogeneity properties of the segmentation technique applied. In this paper, we focus on linear structures, urban structures, agricultural parcels, and forest areas extraction in SAR images.