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.
Highlights
Man-made objects can be divided into linear targets and blob targets
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
Among applications of synthetic aperture radar (SAR) images, the automatic target recognition (ATR) system is of great importance
Summary
Man-made objects can be divided into linear targets and blob 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. 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. The most widely used target detection algorithm is the constant false alarm detection (CFAR)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
