Optical remote sensing image region-of-interest detection method

Abstract

The invention discloses an optical remote sensing image region-of-interest detection method which is applied to high-resolution optical remote sensing image region-of-interest detection. A super-pixel-level image is processed, super pixel-to-pixel saliency mapping is performed to get a pixel-level saliency map, and thus, a region of interest is quickly located from coarse-scale description to fine-scale precise description. The method comprises the following steps: firstly, down-sampling an original image, performing super pixel segmentation, and converting the high-resolution image into a coarse-scale image based on the operations; secondly, getting a super-pixel-level texture feature map and a super-pixel-level color feature map through a structure tensor and a color space background suppression technology based on the generated coarse-scale image; thirdly, getting a super-pixel-level saliency map under the original resolution scale through feature map fusion and up-sampling interpolation; and finally, getting a pixel-level saliency map through super pixel-to-pixel saliency mapping, thus completing pixel-level precise description of the region of interest.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a method for detecting regions of interest in optical remote sensing images based on hierarchical saliency analysis from superpixel level to pixel level. Background technique [0002] Region of interest detection in remote sensing images is one of the applications of remote sensing image analysis and information extraction. With the rapid development of remote sensing technology, the spatial resolution of remote sensing images has become higher and higher, the description of the scene has become more refined, and the amount of data to be processed has also increased. Extracting the urban area and the area to be built in the remote sensing scene as the area of ​​interest can help the land and resources department to count the distribution of urban areas, analyze the growth trend, and formulate development plans. The area of ​​interest can help ...

AI Technical Summary

Problems solved by technology

At present, most of the ROI detection algorithms inspired by visual saliency are based on pixel processing, which has high computational complexity and cannot reasonably and efficiently use the spatial structure information of optical remote sensing images. The integrity of the ROI obtained is poor. Regional distribution sensitivity and other issues

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