Abstract
A novel target detection algorithm for synthetic aperture radar (SAR) images based on an improved visual attention method is proposed in this paper. With the development of SAR technology, target detection algorithms are confronted with many difficulties such as a complicated environment and scarcity of target information. Visual attention of the human visual system can make humans easily focus on key points in a complex picture, and the visual attention algorithm has been used in many fields. However, existing algorithms based on visual attention models cannot obtain satisfactory results for SAR image target detection under complex environmental conditions. After analysing the existing visual attention models, we combine the pyramid model of visual attention with singular value decomposition to simulate the human retina, which can make the visual attention model more suitable to the characteristics of SAR images. We introduce variance weighted information entropy into the model to optimize the detection results. The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses. However, the proposed algorithm can improve both the efficiency and accuracy of target detection in a complicated environment and under weak-target conditions. The experimental results validate the performance of our method.
Highlights
The visual attention model for synthetic aperture radar (SAR) image target detection plays a positive role because the human visual system focuses on the areas of interest and rapidly decides on them [1]
After combining singular value decomposition (SVD) with the Gaussian pyramid model, the variance weighted information entropy (WIE) method is used to distinguish the different types of areas and filter out the regions of interest (ROIs) without targets
We compare the size of the focus of attention (FOA) for two kinds of visual attention algorithm
Summary
The visual attention model for synthetic aperture radar (SAR) image target detection plays a positive role because the human visual system focuses on the areas of interest and rapidly decides on them [1]. A positive effect can be gained by introducing visual attention into target detection in SAR images. The Gaussian pyramid model of visual attention suffers from the difficulty of effectively compressing a SAR image with weak targets. Since the singular value decomposition (SVD) method can keep the important information of a SAR image when the image is compressed [7], combining it with the Gaussian pyramid model can produce images with different compression ratios, which enable the image to retain the target information and well obscure the environment information. After combining SVD with the Gaussian pyramid model, the variance weighted information entropy (WIE) method is used to distinguish the different types of areas and filter out the regions of interest (ROIs) without targets. The Itti model adopts the linear discrete Gaussian filter to perform the smoothing and downsampling in the horizontal and vertical directions of the input image, respectively, and forms eight different resolution subimages [17].
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