Forest change detection from L-band satellite SAR images using iterative histogram matching and thresholding together with data fusion

In this study, we assess the efficiency of using L-band satellite SAR for forest change monitoring, by applying a combination of change detection techniques to SAR backscatter intensity images acquired over Swedish forest. We use a bi-temporal change detection approach based on image rationing. Histogram based techniques are used both for radiometric normalization and thresholding. For a final classification step, we evaluate a data fusion based change detection method that exploits the spatial and spectral information from one or multiple SAR channels. Pre-applied filters are also evaluated as alternatives to tackle low resolution and speckle. HH and HV polarized Fine Beam Dual images, acquired 34 degrees off nadir (FBD34) by the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR), are used to find clear-cuts in Swedish boreal forest. Our results show that clear-cuts can be clearly extracted from L-band SAR data.