Unsupervised Categorization of Forest-Cover Using Multi-Spectral and Hybrid Polarimetric Sar Images

Abstract

In this paper, we propose to distinguish forest-cover in an unsupervised fashion by a combination of passive multi-spectral imagery and active hybrid polarized SAR data. At first, multi-spectral imagery (MSI) is used to separate general vegetation region (e.g., forest, mature grassland, and pre-harvest agricultural fields) from the imaged scene using spectral slopes based rules and support vector machine technique. Then, hybrid polarimetric SAR image of the same region (acquired with a common time stamp) is clustered into three scatter classes, namely, surface, volume, and dihedral, using Stokes parameters based m − δ decomposition. Forest cover is extracted by bi-labeled pixels of the study site that correspond to vegetation (in MSI) and volume scatter (in SAR), which forms a community level classification of forest region. Further, using Wishart derived mean-shift clustering technique, we segregate possible categories of forest clusters within the mapped forest region to obtain a sub-community level classification. Discernible spectral and scattering characteristics of remotely sensed images are explored in our work for identifying forest regions and their possible categories. The proposed method is automated by freeing the manual supervision in selecting seed pixels for training any machine learning technique.