Method and system for visual saliency detection of remote sensing image based on object random walk

Abstract

A method and system for visual saliency detection of remote sensing images based on random walks of objects, including performing multi-scale segmentation, and merging adjacent regions with similar color features at each scale; for the segmentation results at each scale, Extract the visual features of each segmented area separately, and construct the object set at the current scale; for the object set at each scale, calculate the corresponding edge weight through the feature difference between objects, and calculate the transition probability of the attention focus between objects, Obtain the transition probability matrix of the attention focus, calculate the smooth distribution of the attention focus among all objects according to the transition probability matrix of the attention focus, and further calculate the visual salience and normalize it from the probability corresponding to each object in the smooth distribution, and obtain the current The normalized visual saliency map at each scale; the final visual saliency map of the remote sensing image can be obtained by fusing the visual saliency maps at each scale.

Description

technical field [0001] The invention relates to the technical field of image processing, and more specifically, to a method and system for detecting visual saliency of remote sensing images based on object random walk. Background technique [0002] As a major earth observation technology, remote sensing obtains high-resolution optical images that are the most intuitive and true portrayal of the spatial distribution of various objects on the earth's surface. Due to the huge surface area of ​​the earth and the various types and complex changes of ground cover, the high-resolution remote sensing images obtained present a large amount of data, a variety of content, and a complex structure. These characteristics make it time-consuming and difficult to obtain accurate feature descriptions of main objects or regions of interest in images when using computers to automatically process remote sensing images. At present, this phenomenon has become a bottleneck problem restricting the ...

AI Technical Summary

Problems solved by technology

However, the traditional visual attention model based on random walk still has certain limitations when extracting the salient area of ​​the image, mainly in two aspects: First, the traditional model uses pixels as the basic unit to calculate the salient value corresponding to each pixel , in the process of building the Markov chain, too many nodes are set, the transition probability matrix is ​​too large, and the calculation complexity is very high; secondly, the saliency graph calculated by the traditional model has been processed by Gaussian smoothing, and the edges of the saliency region are very Fuzzy, which is very unfavorable for extracting prominent ground objects in high-resolution remote sensing images

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