Open AccessAirfield detection based on JPEG2000 compressed domain
Author(s) -
Duan Chenhui,
Zhao Baojun,
Tang Linbo,
Li Cheng,
Li Chen
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0538
Subject(s) - computer science , jpeg 2000 , header , artificial intelligence , classifier (uml) , exploit , computer vision , compressed sensing , pattern recognition (psychology) , image compression , real time computing , image processing , image (mathematics) , computer network , computer security
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.