Multiscale hierarchical classification of wetland environments using SAR data

A hierarchical, multiresolution classification approach based on an ensemble of neural networks in scale has been implemented and its capability investigated for discriminating between wetland vegetation classes in fully polarimetric, multifrequency synthetic aperture radar (SAR) data. Problems with poorly separated spectral signatures between vegetation types coupled with speckle degrade the quality of standard pixel-based approaches. Because classes differ in spatial extent and are often very irregular in shape, standard region based approaches do not perform well either. The approach used in this study involves sequential separation of land cover classes within the multiscale framework. The algorithm was applied to a coastal salt marsh on the Texas coast where AIRSAR data were acquired in 1996. Classification results were compared to those obtained via a standard maximum likelihood procedure and a simple feedforward neural network formulation