Low-resolution airport target detection method based on hierarchical reinforcement learning

Abstract

The invention provides a low-resolution airport target detection method based on hierarchical reinforcement learning. The method comprises the steps of (1) carrying out super pixel division on an inputted remote sensing image, (2) extracting the boundary super pixel of the input image to construct a background information set, (3) learning the characteristic similarity between each super pixel and a background information set through a minimum distance similarity measurement operator and extracting a deep layer characteristic, (4) defining the ending condition of a learning process, judging whether the step (3) satisfies an ending condition or not, executing a step (6) if so, otherwise, executing a step (5), (5) using the back-propagation theory to act the deep layer characteristic in the step (3) as an reinforcement factor to a local layer input image, and taking the image which is subjected to reinforcement processing as the input image of a next layer learning process, executing the step (1), and continuing a next layer learning, (6) stopping learning, taking the deep layer characteristic learned in the local layer in the step (3) as the salient characteristic of a super pixel, and obtaining a final salient map, and (7) generating the linear feature map of an original image, fusing the linear feature map and the salient map, through salient area positioning and area combination, an airport target area is determined, and the target detection is completed.

Description

technical field [0001] The invention belongs to the application field of computer vision and image processing, and relates to an airport target detection method using layered enhanced learning ideas in low-resolution remote sensing images. The structure of the airport target in the remote sensing image is complex, the resolution of the long-distance imaging is low, the proportion of the target in the large-format remote sensing image is small, and there are buildings similar to the target structure and color in the background. Most of them are based on linear geometric features or template matching methods, which are greatly affected by resolution and imaging quality. In recent years, some scholars have introduced the saliency model into remote sensing target detection. For high-resolution remote sensing targets such as buildings and oil depots, good detection results have been achieved, but for long-distance, low-resolution airports For target detection, the existing salienc...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the problem of airport detection in low-resolution remote sensing images, the present invention proposes a new airport target saliency detection method based on hierarchical reinforcement learning, which can accurately and efficiently solve the problem of airport target detection in large-format remote sensing images

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