Fast target detection method for high-resolution SAR images based on variance weighted information entropy
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RESEARCH
Open Access
Fast target detection method for high-resolution SAR images based on variance weighted information entropy Zongjie Cao*, Yuchen Ge and Jilan Feng
Abstract Since the traditional CFAR algorithm is not suitable for high-resolution target detection of synthetic aperture radar (SAR) images, a new two-stage target detection method based on variance weighted information entropy is proposed in this paper. On the first stage, the regions of interest (ROIs) in SAR image is extracted based on the variance weighted information entropy (WIE), which has been proved to be a simple and effective quantitative description index for the complex degree of infrared image background. Considering that SAR images are nonuniform, an experiment is conducted ahead, in which the value of the variance WIE from a real SAR image in three areas with significant different uniform levels are tested and compared. The results preliminarily verified that the variance WIE is able to measure the complex degree of SAR images. After that, in order to make the segmentation efficient, the rough ROIs are further processed with a series of methods which adjust ROIs into regular pieces. On the second stage, for each of the ROIs, a variational segmentation algorithm based on the Split-Bregman algorithm is adopted to extract the target. In our experiment, the proposed method is tested on two kinds of SAR images, and its effectiveness is successfully demonstrated. Keywords: Synthetic aperture radar; Target detection; Variance weighted information entropy; Variational segmentation
1 Introduction Man-made objects can be divided into linear targets and blob targets. For linear targets, like roads, railways, bridges, and airport runway, they often appear to have obvious characteristics of straight line or curve and can be described approximately through an accurate extraction of the line feature. However, blob targets like tanks, ships, vehicles, aircraft, bunkers, oil depots, power plants, and other types of construction usually have a reunion in the spatial distribution, which means that the targets are located in a rectangular area in the image as a whole. This paper is focused on the detection of blob targets. Among applications of synthetic aperture radar (SAR) images, the automatic target recognition (ATR) system is of great importance. The target detection of SAR images, serving as the first stage of ATR systems, provides a basis for the validity of subsequent recognition. By now, the most widely used target detection algorithm is the constant false alarm detection (CFAR). * Correspondence: [email protected] School of Electronic Engineering, UESTC, Chengdu 611731, China
CFAR algorithms detect targets by pixel-wise threshold operation, in which the threshold is adaptively computed for each pixel according to the statistical property of pixels within a local window [1-7]. However, since CFAR is a kind of pixel level methods of target detection, it is not suitable for SAR images of high resolution in reality. First of all, the serious s
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