Airfield detection based on JPEG2000 compressed domain. Issue 21 (10th October 2019)

• Record Type: Journal Article
• Authors: Duan, Chenhui; Zhao, Baojun; Tang, Linbo; Li, Cheng; Li, Chen
• Is Part Of: Journal of engineering. Volume 2019:Issue 21(2019)
• Page Start: 7331
• Page End: 7334
• Publication Date: 2019-10-10
• Subjects: image classification -- wavelet transforms -- image segmentation -- object detection -- support vector machines -- feature extraction -- image coding -- geophysical image processing -- data compression -- remote sensing; line segment detection -- airfield candidates -- RS images -- nonstructural regions -- structural saliency map -- raw JPEG2000 code streams -- airfield detection framework -- saliency method -- on‐orbit specific target detection -- optical remote sensing images -- JPEG2000 compressed domain -- detection rate -- final detection results; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2019.0538
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : With the rapid increase of optical remote sensing (RS) images, on‐orbit specific target detection has been facing more and more challenges: limited processing environment, wireless bandwidth, space, power and storage. To resolve this issue, visual characteristics in the compressed domain are exploited to represent and recognise specific objects. In this study, a new saliency method and an airfield detection framework in the JPEG2000 compressed domain is proposed. First, the authors exploit the header information in raw JPEG2000 code streams to generate structural saliency map, and a great deal of non‐structural regions in RS images are eliminated. Second, airfield candidates are extracted from low frequency wavelet sub‐bands using line segment detection and the parallel density model. Finally, the geometrical properties of airfield candidates are represented by the speed‐up robust features descriptor, and the support vector machine classifier is utilised to gain the final detection results. The proposed framework is evaluated on self‐made dataset. Compared with the existing relevant state‐of‐the‐art approaches, the proposed method reduces the processing time by 60% while guaranteeing the detection rate.