A novel method for speckle noise reduction and ship target detection in SAR images

Huang, Shiqi; Liu, Daizhi; Gao, Guiqing; Guo, Xi-jian

doi:10.1016/j.patcog.2009.01.013

Cited by 69 publications

(36 citation statements)

References 22 publications

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“…The main difference of them is the different clutter mean estimation algorithms. In this experimentation, the CA-CFAR algorithm, the two-parameter CFAR (TP-CFAR) algorithm [8] and the coherence-based CFAR (CCFAR) algorithm [9] are used to test the new algorithm. The experimental data comes from the SAR image databank of the moving and stationary target acquisition and recognition (MSTAR), and they are shown in Fig.2.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

SAR Target Detection Method Based on Empirical Mode Decomposition

Huang

Wang

et al. 2012

AEF

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Abstract. Empirical mode decomposition (EMD) is a new signal processing theory, and it is very much fitting for non-stationary signal processing, such as radar signal. So this paper proposes the new synthetic aperture radar (SAR) image target detection algorithm after analyzing the characteristics of EMD and SAR images. The proposed method performs the EMD operation, feature extraction, election and fusion, which can reduce the affection of speckle. Experimental results show that the proposed method is very effective.

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Section: Experimental Results and Analysismentioning

confidence: 99%

SAR Target Detection Method Based on Empirical Mode Decomposition

Huang

Wang

et al. 2012

AEF

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“…This is caused by the complexity of the imaging procedure and the randomness of ocean states, which is common in high-resolution polarimetric SAR images. However, these undetected ships might still be distinguishable by manual interpretation [31], thanks to the visual attention mechanism in the human brain. The basic idea of visual attention is to inhibit the homogeneous background or identical areas while highlighting the distinctive objects through a "center-surround" mechanism [32].…”

Section: Methodsmentioning

confidence: 99%

Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model

Zhang

Wang

et al. 2016

Remote Sensing

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Abstract:A few previous studies have illustrated the potentials of compact polarimetric Synthetic Aperture Radar (CP SAR) in ship detection. In this paper, we design a ship detection algorithm of CP SAR from the perspective of computer vision. A ship detection algorithm using the pulsed cosine transform (PCT) visual attention model is proposed to suppress background clutter and highlight conspicuous ship targets. It is the first time that a visual attention model is introduced to CP SAR application. The proposed algorithm is a quick and complete framework for practical use. Polarimetric features-the relative phase δ and volume scattering component-are extracted from m-δ decomposition to eliminate false alarms and modify the PCT model. The constant false alarm rate (CFAR) algorithm based on lognormal distribution is adopted to detect ship targets, after a clutter distribution fitting procedure of the modified saliency map. The proposed method is then tested on three simulated circular-transmit-linear-receive (CTLR) mode images, which covering East Sea of China. Compared with the detection results of SPAN and the saliency map with only single-channel amplitude, the proposed method achieves the highest detection rates and the lowest misidentification rate and highest figure of merit, proving the effectiveness of polarimetric information of compact polarimetric SAR ship detection and the enhancement from the visual attention model.

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“…Although this method is able to considerably remove additive white Gaussian noise (AWGN) as well as keeping the image texture relatively unchanged, it has been only tested on single-band images. Authors in Huang et al [2009] develop a technique to overcome the problem of speckle noises which infect the synthetic aperture radar (SAR) images. Their main goal is to improve the detection ratio for ship targets from SAR imaging mechanism.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Adaptive Restoration of Multispectral Datasets used for SVM classification

Zehtabian

Nazari

Ghassemian

et al. 2015

European Journal of Remote Sensing

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Removing noise from images while keeping its important details unchanged is a challenging issue in image restoration. In this paper, we propose a novel approach based on partial differential equations (PDE) in order to mitigate three well-known types of noises from remote sensing data while important features such as edges are preserved. In the presented method, after performing the Watershed-based segmentation as a preprocessing step, optimum values of PDE parameters are adaptively found based on the noise type and the image texture. In order to evaluate the performance of the proposed algorithm, Peak Signal-to-Noise Ratio (PSNR) criterion is applied. Moreover, feeding the original/noisy/denoised images into SVM classifier and exploring the classification ratios are suggested as an application-based assessment. The gained results prove a considerable enhancement both in quantitative metrics (PSNR and MSE) and SVM classification ratios (from 71.71% to 95.07%).

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A novel method for speckle noise reduction and ship target detection in SAR images

Cited by 69 publications

References 22 publications

SAR Target Detection Method Based on Empirical Mode Decomposition

SAR Target Detection Method Based on Empirical Mode Decomposition

Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model

Adaptive Restoration of Multispectral Datasets used for SVM classification

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