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Proceedings Paper

Regions-of-interest extraction from remote sensing imageries using visual attention modelling
Author(s): Hui Li Tan; Jiayuan Fan; Maria Toomik; Shijian Lu
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Paper Abstract

Processing and analysing large volume of remote sensing data is both labour intensive and time consuming. Therefore, there is a need to effectively and efficiently identify meaningful regions in these remote sensing data for timely resource management. In this paper, we propose a visual attention model for identifying regions-of-interest in remote sensing data. The proposed model incorporates both bottom-up spatial saliency and top-down objectness, by fusing a co-occurrence histogram saliency model with the BING objectness model. The co-occurrence histogram saliency model is constructed by first building a 2D co-occurrence histogram that captures co-occurrence and occurrence of image intensities, and then using the 2D co-occurrence histogram to model local and global saliency. On the other hand, the BING objectness model is constructed by resizing image intensities in variable-sized windows to 8x8 windows, and then using the norms of the gradients in the 8x8 windows as features to train a generic objectness measure. Our experimental results show that the proposed model can effectively and efficiently identify regions-of-interest in remote sensing data. The proposed model may be applied in various remote sensing applications such as anomaly detection, urban area detection, target detection, or land use classification.

Paper Details

Date Published: 18 October 2016
PDF: 7 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040N (18 October 2016); doi: 10.1117/12.2240749
Show Author Affiliations
Hui Li Tan, Agency for Science, Technology and Research (A*STAR) (Singapore)
Jiayuan Fan, Agency for Science, Technology and Research (A*STAR) (Singapore)
Maria Toomik, Univ. College London (United Kingdom)
Agency for Science, Technology and Research (A*STAR) (Singapore)
Shijian Lu, Agency for Science, Technology and Research (A*STAR) (Singapore)


Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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