|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images|
Synthetic aperture radar (SAR)
|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.|
|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.|
|Rights:||The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.|
|Gao_etal_CognComput_2016.pdf||2.81 MB||Adobe PDF||Under Permanent Embargo Request a copy|
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact email@example.com providing details and we will remove the Work from public display in STORRE and investigate your claim.