A Novel Fuzzy-Based Remote Sensing Image Segmentation Method

Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to inccrease the segmentation accuracy; however, the critical point of these approaches is the high com putational complexity, which affects time and memory consumption. In order to overcome this crit icality, we propose a fuzzy-based image segmentation framework implemented in a GIS-based plat form for remotely sensed images; furthermore, the proposed model allows us to evaluate the relia bility of the segmentation. The Fast Generalized Fuzzy c-means algorithm is implemented to seg ment images in order to detect local spatial relations between pixels and the Triple Center Relation validity index is used to find the optimal number of clusters. The framework elaborates the compo site index to be analyzed starting by multiband remotely sensed images. For each cluster, a seg mented image is obtained in which the pixel value represents, transformed into gray levels, the graph belonging to the cluster. A final thematic map is built in which the pixels are classified based on the assignment to the cluster to which they belong with the highest membership degree. In ad dition, the reliability of the classification is estimated by associating each class with the average of the membership degrees of the pixels assigned to it. The method was tested in the study area con sisting of the south-western districts of the city of Naples (Italy) for the segmentation of composite indices maps determined by multiband remote sensing images. The segmentation results are con sistent with the segmentations of the study area by morphological and urban characteristics, carried out by domain experts. The high computational speed of the proposed image segmentation method allows it to be applied to massive high-resolution remote sensing images.