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Citation

Gao F, Ma F, Zhang Y, Wang J, Sun J, Yang E & Hussain A (2016) Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images. Cognitive Computation, 8 (5), pp. 955-966. https://doi.org/10.1007/s12559-016-9405-9

Abstract
High-resolution synthetic aperture radar (SAR) can provide a rich information source for target detection and greatly increase the types and number of target characteristics. How to efficiently extract the target of interest from large amounts of SAR images is the main research issue. Inspired by the biological visual systems, researchers have put forward a variety of biologically inspired visual models for target detection, such as classical saliency map and HMAX. But these methods only model the retina or visual cortex in the visual system, which limit their ability to extract and integrate targets characteristics; thus, their detection accuracy and efficiency can be easily disturbed in complex environment. Based on the analysis of retina and visual cortex in biological visual systems, a progressive enhancement detection method for SAR targets is proposed in this paper. The detection process is divided into RET, PVC, and AVC three stages which simulate the information processing chain of retina, primary and advanced visual cortex, respectively. RET stage is responsible for eliminating the redundant information of input SAR image, enhancing inputs’ features, and transforming them to excitation signals. PVC stage obtains primary features through the competition mechanism between the neurons and the combination of characteristics, and then completes the rough detection. In the AVC stage, the neurons with more receptive field compound more precise advanced features, completing the final fine detection. The experimental results obtained in this study show that the proposed approach has better detection results in comparison with the traditional methods in complex scenes.

Keywords
Cortex-like mechanisms; Synthetic aperture radar (SAR); Hierarchical models; Target detection

Journal
Cognitive Computation: Volume 8, Issue 5