An enhanced rule-based fuzzy segmentation approach for automated urban feature extraction using high-resolution satellite imagery

Abstract

A fuzzy segmentation approach based on rule-based fuzzy rules is proposed in this study to obtain urban features using high-resolution satellite images. Multiresolution segmentation and spectral difference segmentation are combined to effectively identify and classify houses, roads, trees, and agricultural fields in urban areas and rural farms.Afuzzy rule setwas developed using satellite datasets from IKONOS, LISS IV, and WorldView-2, improving classification accuracy. In this work, buildings were extracted from IKONOS images, agricultural fields were extracted from LISS IV images, and roads and vegetation were extracted from WorldView-2 images. The map updating capability was demonstrated for 1:2500 and 1:1000 scales, respectively, for buildings and agricultural fields. Furthermore, the gray-level cooccurrence matrix was employed to enhance classification reliability and mitigate spectral confusion. By automating the process, the need for additional GIS data is reduced, making it a cost-effective, scalable, and efficient approach. Compared to traditional manual feature extraction methods, this method is an effective alternative in urban planning, land use mapping, and environmental monitoring.