The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest

Abstract

The objective of this paper is to study the use of a decision tree classifier and multiscale texture measures to extract thematic information on the tropical vegetation cover from the Global Rain Forest Mapping (GRFM) JERS-1 SAR mosaics. The authors focus their study on a coastal region of Gabon, which has a variety of land cover types common to most tropical regions. A decision tree classifier does not assume a particular probability density distribution of the input data, and is thus well adapted for SAR image classification. A total of seven features, including wavelet-based multiscale texture measures (at scales of 200, 400, and 800 m) and multiscale multitemporal amplitude data (two dates at scales 100 and 400 m), are used to discriminate the land cover classes of interest. Among these layers, the best features for separating classes are found by constructing exploratory decision trees from various feature combinations. The decision tree structure stability is then investigated by interchanging the role of the training samples for decision tree growth and testing. They show that the construction of exploratory decision trees can improve the classification results. The analysis also proves that the radar backscatter amplitude is important for separating basic land cover categories such as savannas, forests, and flooded vegetation. Texture is found to be useful for refining flooded vegetation classes. Temporal information from SAR images of two different dates is explicitly used in the decision tree structure to identify swamps and temporarily flooded vegetation.