Abstract
Visual attention is an attractive technique to derive important and prominent information from a scene in natural pictures. As a visual attention approach, spectral residual (SR) model is adapted to extract the residential regions from GF-1 satellite images in this paper. Specifically, we analyzed the impact of both different combinations of GF-1 satellite image bands and threshold algorithms on rural residential region detection. In addition, the adapted approach is compared with related visual attention methods in terms of both quantitative and qualitative detection effectiveness. Experimental results showed that the SR model coupled withred,green,andbluebands in GF-1 images and Otsu threshold algorithm achieved the best results and is suitable to quickly extract rural residential regions from GF-1 images.
Highlights
The detection of residential regions has very important significance in urban development, map updating, and disaster management
Three experiments are conducted to evaluate the performance of the spectral residual (SR) model using GF-1 satellite imagery to extract residential regions
The SR model is compared with other visual attention models to extract residential regions
Summary
The detection of residential regions has very important significance in urban development, map updating, and disaster management. A wide range of automatic residential region detection techniques have been reported [1,2,3,4,5,6,7] They could fall in the following three categories: indexbased methods, image classification, and visual attention models. Visual attention is a technique to derive important and prominent information from a scene in natural pictures This type of method has the potential to discriminate the residential regions from the background in satellite images. The main advantage of the SR model is its generality
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