Morphological profiles based on differently shaped structuring elements for classi fication of images with very high spatial resolution

Morphological profiles (MPs) have been proposed for the segmentation and classification of high spatial resolution (HSR) images. A shortcoming of the originally proposed MPs is that the profiles were only based on structuring elements (SEs) of one particular shape, suggesting that such MPs may not be suitable for detecting different shapes in images. To better fit several shapes in a given image, a new approach based on mathematical morphology is proposed to extract structural information from HSR images and consequently yield new versions of MPs. The classification results for the new MPs are compared with the classification of spatial features extracted with the use of pixel shape index, gray level co-occurrence matrix, and previously proposed MPs. The experimental results suggest the following: 1) structural and spectral features can complement each other and their integration can improve classification accuracy and 2) MPs constructed by differently shaped SEs are less sensitive to salt-and-pepper noise than those constructed by fixed-shaped SEs.