Evaluation of multiscale morphological segmentation of multispectral imagery for land cover classification

This paper investigates segmentation of multispectral images using multiscale watershed transformation for land cover classification with SPOT 5 multispectral data. The multiscale gradient operator was computed separately for each band, and then resulting multiple gradient images were combined by averaging of magnitude of the gradient components to create a single-value gradient image. The gradient image was filtered to eliminate local minima. The watershed transformation was used for segmentation of the filtered gradient image. The results indicate that the use of multiscale gradients for watershed segmentation could overcome the over-segmentation effect and achieve more accurate segmentation result. The incorporation of contextual information from multiscale morphological segmentation in image classification substantially improves the classification accuracy, compared to pixel-wise classification.