SAR image man-made target detection method based on visual contrast and information entropy

Abstract

The invention discloses an SAR image man-made target detection method based on visual contrast and information entropy. Local contrast information of an image is extracted by simulating simple cells in a primary visual cortex according to biological visual features; a computing pool model is learnt and S units are integrated into a complex unit C, and Gaussian filtering processing is performed so that a global salient graph is generated; threshold segmentation is performed on the salient graph according to the maximum information entropy principle; isolated pixel sets are eliminated by morphological filtering processing, and connectivity of the pixel sets is enhanced; and target edge information is extracted and displayed on an original SAR image so that a target detection result is obtained. The achieved beneficial effects are that effective detection of the man-made target in the SAR image can be realized, computation burden is low, processing efficiency is high and project implementation is facilitated, and thus the method can be used for rapid detection of the small man-made target.

Description

technical field [0001] The invention relates to a synthetic aperture radar (Synthetic Aperture Radar, SAR) image artificial target rapid detection method based on visual contrast and information entropy, which belongs to the technical field of remote sensing image processing. Background technique [0002] Object detection is an important research topic in the field of SAR image interpretation. Since SAR technology has reached the practical level, the research on SAR image object detection has been widely concerned. Man-made targets such as buildings, bridges, vehicles, and ships are the key monitoring objects in SAR earth observation. The detection and identification of such targets are of great significance in both military and civilian use, and have been widely used in military reconnaissance and urban planning. , disaster monitoring, resource survey and other fields. [0003] The existing SAR image target detection algorithms mainly include: target detection algorithms b...

AI Technical Summary

Problems solved by technology

The most representative model is the model based on the underlying visual features proposed by Itti et al. in 1998 (Itti model for short), see IttiL, KochC, NieburE. -1259. There is also the spectral residual model based on spatial frequency domain analysis (SR model for short) proposed by Hou et al. in 2007, see HouX, ZhangL.SaliencyDetection: ASpectralResidualApproach[C].2007IEEEConferenceonComputerVisionandPatternRecognition,IEEEComputerSociety,2007:1-8 .However, since most of the existing visual attention models are aimed at natural images, the application in SAR images is relatively limited, and the detection results are not satisfactory

Recently, some scholars have improved the Itti model, but the processing efficiency is low and it is not convenient for engineering implementation

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