Residential area extraction based on saliency analysis for high spatial resolution remote sensing images |
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Affiliation: | 1. Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China;2. School of Information and Mechatronics, Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Republic of Korea;1. School of Computer and Electronic Information, Guangxi University, Nanning, Guangxi 530004, China;2. Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA |
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Abstract: | Traditional residential area extraction methods for remote sensing image depend on classification, segmentation and prior knowledge which are time-consuming and difficult to build. In this paper, an efficient, saliency analysis-based residential area extraction method is proposed. In the proposed model, an adaptive directional prediction-based lifting wavelet transform (ADP-LWT) is introduced to obtain the orientation feature. A logarithm co-occurrence histogram is employed to compute the intensity feature. The color opponency and diagram objection based on the information are proposed to extract color feature from the contrast in the red–green opponent channel. The saliency map is obtained through a weighted combination based on the feature competition and the residential area is extracted by saliency map threshold segmentation. The experimental results reveal that the residential area extracted by our model has more demarcated boundaries and better performance in background subtraction. |
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Keywords: | Remote sensing image processing Residential area extraction Saliency analysis Lifting wavelet transform Logarithm co-occurrence histogram Color opponency Feature competition Threshold |
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