Regions-of-interest extraction from remote sensing imageries using visual attention modelling

Hui Li Tan; Jiayuan Fan; Maria Toomik; Shijian Lu

doi:10.1117/12.2240749

18 October 2016 Regions-of-interest extraction from remote sensing imageries using visual attention modelling

Hui Li Tan, Jiayuan Fan, Maria Toomik, Shijian Lu

Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 100040N (2016) https://doi.org/10.1117/12.2240749
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

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.

Citation Download Citation

Hui Li Tan, Jiayuan Fan, Maria Toomik, and Shijian Lu "Regions-of-interest extraction from remote sensing imageries using visual attention modelling", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040N (18 October 2016); https://doi.org/10.1117/12.2240749

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KEYWORDS

Remote sensing

Data modeling

Visualization

Visual process modeling

Target detection

Modeling

Ecosystems

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