Abstract
The statistical region merging (SRM) algorithm exhibits efficient performance in solving significant noise corruption and does not depend on the data distribution. These advantages make SRM suitable for the segmentation of synthetic aperture radar (SAR) images, which are characterized by speckle noise and different distributions of various data types and spatial resolutions. However, the original SRM algorithm is designed for RGB and gray images characterized by additive noise and having a range of [0, 255]. In this letter, the SRM algorithm is generalized so that it can be applied to images with larger range and multiplicative noise. The original 4-neighborhood models are also generalized into 8-neighborhood models. The effectiveness of the generalized SRM (GSRM) algorithm is demonstrated by AirSAR and ESAR L-band Polarimetric SAR (PolSAR) data. Given that the input data of the GSRM algorithm can be single- or multi-dimensional, the proposed GSRM algorithm can be used for single- and multi-polarized as well as for fully polarimetric SAR data.
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