Classification of high resolution satellite images using spatial constraints-based fuzzy clustering

Abstract

A spatial constraints-based fuzzy clustering technique is introduced in the paper and the target application is classification of high resolution multispectral satellite images. This fuzzy-C-means (FCM) technique enhances the classification results with the help of a weighted membership function (Wmf). Initially, spatial fuzzy clustering (FC) is used to segment the targeted vegetation areas with the surrounding low vegetation areas, which include the information of spatial constraints (SCs). The performance of the FCM image segmentation is subject to appropriate initialization of Wmf and SC. It is able to evolve directly from the initial segmentation by spatial fuzzy clustering. The controlling parameters in fuzziness of the FCM approach, Wmf and SC, help to estimate the segmented road results, then the Stentiford thinning algorithm is used to estimate the road network from the classified results. Such improvements facilitate FCM method manipulation and lead to segmentation that is more robust. The results confirm its effectiveness for satellite image classification, which extracts useful information in suburban and urban areas. The proposed approach, spatial constraint-based fuzzy clustering with a weighted membership function (SCFCWmf), has been used to extract the information of healthy trees with vegetation and shadows showing elevated features in satellite images. The performance values of quality assessment parameters show a good degree of accuracy for segmented roads using the proposed hybrid SCFCWmf-MO (morphological operations) approach which also occluded nonroad parts.