Road detection in spaceborne SAR images using a genetic algorithm

• Published in: IEEE transactions on geoscience and remote sensing Vol. 40; no. 1; pp. 22 - 29
• Main Authors: JEON, Byoung-Ki, JANG, Jeong-Hun, HONG, Ki-Sang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied geophysics; Computational efficiency; Design methodology; Detectors; Earth sciences; Earth, ocean, space; Exact sciences and technology; Genetic algorithms; Image segmentation; Internal geophysics; Mathematical models; Optimization; Optimization methods; Radar detection; Roads; Segments; Spaceborne radar; Studies; Synthetic aperture radar; algorithms; models; synthetic aperture radar; optimization; Space remote sensing; roads; methodology
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/36.981346

This paper presents a technique for the detection of roads in a spaceborne synthetic aperture radar (SAR) image using a genetic algorithm (GA). Roads in a spaceborne SAR image can be modeled as curvilinear structures that possess width. Curve segments, which represent the candidate positions for roads, are extracted from the image using a curvilinear structure detector, and the roads are accurately detected by grouping those curve segments. For this purpose, the authors designed a grouping method based on a GA, which is a global optimization method. They combined perceptual grouping factors with it and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process, which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. The authors applied their method to ERS-1 SAR and SIR-C/X-SAR images that have a resolution of about 30 m. The experimental results show that our method can accurately detect road networks as well as single-track roads and is much faster than a globally applied GA approach.