It still remains a big challenge to accurately identify the geospatial objects with well-regulated outlines within remote sensing (RS) images such as residential buildings, factory storage buildings, highways, local roads, cars, and planes. In this paper, a novel spatial feature index, which is named regular shape similarity index (RSSI), is defined to address the challenge. It represents the ratio between the area of an object and its minimum bounding shape area. The application of RSSI in identifying objects with different shapes is discussed, and its capability is found to be a great supplement to the existing spatial feature hierarchy. An approach combining RSSI with object-based image analysis (OBIA) technology is proposed for image object extraction. A Web service for RSSI calculation is developed and integrated into a Web OBIA system. In the system, four experiments extracting factory storage buildings, residential buildings, roads, and planes, respectively, are conducted on three large-scale high-resolution RS images. In each experiment, two tests, i.e., one using traditional spatial features and the other using RSSI, are performed and compared. The results show that RSSI improves the accuracy of regular object extraction.
Sun, Z., Fang, H., Deng, M., Chen, A., Yue, P. and Di, L.. "Regular Shape Similarity Index: A Novel Index for Accurate Extraction of Regular Objects From Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, v.53, 2015, p. 3737. doi:10.1109/TGRS.2014.2382566This material is based upon work supported by the National Science Foundation under Grant No. 1440294. Opinions, findings, conclusions or recommendations expressed are those of the authors and do not reflect the views of the NSF.