Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation

In this paper, a multiscale texture-based classifier for mapping tropical forest land cover types is discussed. The classifier was implemented using the Japanese Earth Remote Sensing Satellite (JERS-1) 100 m resolution radar data acquired over the Amazon Rainforest as part of the Global Rainforest Mapping (GRFM) Project. Demonstrated here is the use of the information content present in different texture measurements at different scales to separate three categories of land cover types: forest from nonforest, terre firme from floodplain vegetation, and grassland from woodland savanna. Various combinations of first-order image statistics known as texture measures were used at different scales as feature dimensions to aid the class discrimination. Eight of the most common first-order texture measures found in the literature were used. The best combination of texture measures at each scale were determined by employing a class separability test using the Bhattachuryya distance. The results were then used as in...