Visual attention model based mining area recognition on massive high-resolution remote sensing images

With the development of remote sensing technology, satellite images with the characteristics of multi-scale, multi-band, and multi-date make it tend to be big data. So how to raise the extraction speed, precision and automatic degree of salient objects from high-resolution remote sensing images become urgent problems. Based on the analysis using an Itti visual attention model for natural image processing, we achieved improvements in two aspects: (1) the selection of salient regions based on elevation data, and (2) the segmentation of salient regions using the Snake model for precise object contour extraction. Tests on the extraction of 2.5 m high-resolution remote sensing image data in the rare earth mining area in Dingnan County, Jiangxi Province showed a false alarm rate of 14.8 % and a missing alarm rate of 8.4 % in the extraction of mine quantity data. The proposed method could be useful for improving the speed, precision and automatic extraction of salient objects from high-resolution remote sensing images as well as the boundary information of salient objects that are based on a visual attention model.