Aircraft Detection Method Based on Corner and Edge Information Fusion in Remote Sensing Image

Abstract

The invention discloses a remote sensing image airplane detection method based on fusion of angle points and edge information. The method comprises the following steps of: executing edge detection on a remote sensing image; performing binarization on the detected remote sensing image; executing Harris angle point detection on the binarized remote sensing image; selecting regions in which the number of the angle points is in a predetermined range as candidate airplane regions; removing false target regions according to the number of bright pixels in each candidate airplane region; clustering the obtained image and marking a target airplane position, thus finally obtaining the number of obtained clusters and a central position of each cluster, wherein the number of the obtained clusters is the number of detected airplanes, and the central position of each cluster is the central position of a target airplane. The invention further discloses a remote sensing image airplane detection system comprising an edge connection unit, a binarization processing unit, an angle point detection unit, a candidate airplane region selection unit and a clustering unit which are orderly connected. Compared with the prior art, the method has the advantages of better detection effect and higher detection efficiency.

Description

technical field [0001] The invention relates to an image detection method, in particular to a method for using remote sensing images for aircraft detection, and belongs to the technical field of image recognition. Background technique [0002] With the rapid development of remote sensing technology, locating and identifying objects of interest in remote sensing images has become an important research direction. Using remote sensing images to detect targets has broad application prospects in both military and civilian fields. Especially in the military field, the use of remote sensing images to detect important military targets has been applied in national defense construction. As an important military target, the detection and identification of aircraft has always been a research hotspot. [0003] Object detection strategies are generally divided into two categories: one is called bottom-up data-driven strategy, and the other is called top-down knowledge-driven strategy. ...

AI Technical Summary

Problems solved by technology

However, due to the influence of remote sensing image quality and aircraft shadows, usually the aircraft target will be broken into several areas after segmentation, and it is difficult to completely extract the edge contour of the aircraft target, so the usual method is to merge areas or connect edges

This method is not only very complicated to implement but also has weak anti-interference ability. At the same time, because there are many types of aircraft, it is difficult to use a unified template to detect all aircraft targets, so the detection results are often unsatisfactory.

A document ([Cai Hongping, Geng Zhenwei, Li Yi. A New Method for Aircraft Detection in Remote Sensing Images—Circumferential Frequency Filtering [J]. Signal Processing, Vol. 23, No. 4, 2007, 8:539-543]) proposed An aircraft detection method that adopts a top-down knowledge-driven strategy, but this method has a strong dependence on the gray value of the image, so for images with camouflaged aircraft targets or aircraft with weak distinction from the background, Many missed cases

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