High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model
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High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model Tian-jian Luo 1,2 Received: 23 September 2019 / Revised: 22 June 2020 / Accepted: 4 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
The high-resolution synthetic aperture radar (SAR) images usually contain inhomogeneous coherent speckle noises. For the high-resolution SAR image segmentation with such noises, the conventional methods based on pulse coupled neural networks (PCNN) have to face heavy parameters with a low efficiency. In order to solve the problems, this paper proposes a novel SAR image segmentation algorithm based on non-subsampling Contourlet transform (NSCT) denoising and quantum immune genetic algorithm (QIGA) improved PCNN models. The proposed method first denoising the SAR images for a pre-processing based on NSCT. Then, by using the QIGA to select parameters for the PCNN models, such models self-adaptively select the suitable parameters for segmentation of SAR images with different scenes. This method decreases the number of parameters in the PCNN models and improves the efficiency of PCNN models. At last, by using the optimal threshold to binary the segmented SAR images, the small objects and large scales from the original SAR images will be segmented. To validate the feasibility and effectiveness of the proposed algorithm, four different comparable experiments are applied to validate the proposed algorithm. Experimental results have shown that NSCT pre-processing has a better performance for coherent speckle noises suppression, and QIGA-PCNN model based on denoised SAR images has an obvious segmentation performance improvement on region consistency and region contrast than state-of-the-arts methods. Besides, the segmentation efficiency is also improved than conventional PCNN model, and the level of time complexity meets the stateof-the-arts methods. Our proposed NSCT+QIGA-PCNN model can be used for small object segmentation and large scale segmentation in high-resolution SAR images. The segmented results will be further used for object classification and recognition, regions of interest extraction, and moving object detection and tracking. Keywords Pulse coupled neural networks . Quantum immune genetic algorithm . Nonsubsampling contourlet transformation . SAR images segmentation . SAR images denoising
* Tian-jian Luo [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction Synthetic aperture radar (SAR) technology constructs images by using microwave bands, which provides the characteristics of high-resolution, all-weather, all-time, multi-views, and large areas. Therefore, the SAR images are widely applied in military reconnaissance, resource exploration, and terrain determination [10, 38, 43]. Recently, high-resolution SAR images construction technology has a rapid development. However, the high-resolution SAR images processing technologies fall beh
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