This paper aims to present a novel feature design that is able to precisely describe salient objects in images. With the development of space survey, sensor and information acquisition technologies, more complex objects appear in high-resolution remote sensing images. Traditional visual features are no longer precise enough to describe the images.
A novel remote sensing image retrieval method based on VSP (visual salient point) features is proposed in this paper. A key point detector and descriptor are used to extract the critical features and their descriptors in remote sensing images. A visual attention model is adopted to calculate the saliency map of the images, separating the salient regions from the background in the images. The key points in the salient regions are then extracted and defined as VSPs. The VSP features can then be constructed. The similarity between images is measured using the VSP features.
According to the experiment results, compared with traditional visual features, VSP features are more precise and stable in representing diverse remote sensing images. The proposed method performs better than the traditional methods in image retrieval precision.
This paper presents a novel remote sensing image retrieval method based on VSP features.
The authors thank the anonymous reviewers for their comments and suggestions. This research is supported in part by the National Basic Research Program of China (No. 2010CB731800); National Science and Technology Specific Projects (No. 2012YQ16018505 and No. 2013BAH42F03); National Natural Science Foundation of China (No. 61172174); Program for New Century Excellent Talents in University (No. NCET-12-0426); the Fundamental Research Fund for the Central Universities (No. 201121302020008) and Program for Luojia young scholars of Wuhan University.
Wang, X., Shao, Z., Zhou, X. and Liu, J. (2014), "A novel remote sensing image retrieval method based on visual salient point features", Sensor Review, Vol. 34 No. 4, pp. 349-359. https://doi.org/10.1108/SR-03-2013-640Download as .RIS
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