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. We focus our 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. We 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.

A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

“…Notably, data from a number of past and current spaceborne SAR systems-Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR), European Remote Sensing (ERS-1 and -2), Advanced Synthetic Aperture Radar (ASAR), Japanese Earth Resources Satellite (JERS-1), RADARSAT-1 and -2, Advanced Land Observation Satellite (ALOS-1)-are commonly in use and applied at regional-scales, with very few studies addressing global-scale mapping, e.g., [73]. Studies have covered a variety of themes related to land cover, including improved land cover classifications [35,74], forest cover classifications [75], grassland monitoring [47], identification of degraded woodlands [27,76,77] and mapping deforestation [78] and successional forest dynamics [11]. Similarly, land use-specific studies have focussed on various themes, including urban land use analysis [79,80], classification of agricultural areas [81], mapping and monitoring specific crop types (e.g., rice [82][83][84]), etc.…”

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

“…All topographic metrics were calculated using the open source GIS SAGA (System for Automated Geoscientific Analyses, version 2.2.3) [57]. The predictor variables listed in Table 3 also include the coefficient of variation from a 3 × 3-pixel moving window applied to the HH and HV PALSAR images; it was used as a texture metric to evaluate how spatial heterogeneity in backscatter intensity can contribute to improving the discrimination of the wetland classes [58,59]. PALSAR L-band polarization intensity ratio HV/HH was selected for its effectiveness in discriminating flooded from non-flooded vegetation and water [47], while HH/HV demonstrated similar characteristics using SAR C-band data [60].…”

Mapping tropical forest cover and deforestation using synthetic aperture radar (SAR) images

“…Synthetic aperture radar (SAR) has been used to quantify forest cover (Ranson and Sun 1994;Dobson et al 1996;Pierce et al 1998;Simard et al 2000) and forest cover change (Ranson and Sun 1997;Rignot et al 1997;Saatchi et al 1997). SAR data used in those studies are from experimental (Ranson and Sun 1997;Rignot et al 1997;Saatchi et al 1997) and commercial SAR missions (Fransson et al 2007;Santos et al 2008).…”