Scheme of Parameter Estimation for Generalized Gamma Distribution and Its Application to Ship Detection in SAR Images

Gao, Gui; Ouyang, Kewei; Luo, Yongbo; Liang, Sheng; Zhou, Shilin

doi:10.1109/tgrs.2016.2634862

Cited by 93 publications

(40 citation statements)

References 57 publications

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“…In remote sensing applications, Gamma distribution has been used to model sea [37] and land [8] SAR images, and ultrasound speckle noise [5]. The univariate gamma distribution pdf can be expressed as [36]:…”

Section: Gamma Distribution Familymentioning

confidence: 99%

Generalized Bayesian Model Selection for Speckle on Remote Sensing Images

Karakuş

Kuruoğlu

Altınkaya

2019

IEEE Trans. on Image Process.

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Synthetic aperture radar (SAR) and ultrasound (US) are two important active imaging techniques for remote sensing, both of which are subject to speckle noise caused by coherent summation of back-scattered waves and subsequent nonlinear envelope transformations. Estimating the characteristics of this multiplicative noise is crucial to develop denoising methods and to improve statistical inference from remote sensing images. In this study, reversible jump Markov chain Monte Carlo (RJM-CMC) algorithm has been used with a wider interpretation and a recently proposed RJMCMC based Bayesian approach, trans-space RJMCMC, has been utilized. The proposed method provides an automatic model class selection mechanism for remote sensing images of SAR and US where the model class space consists of popular envelope distribution families. The proposed method estimates the correct distribution family, as well as the shape and the scale parameters, avoiding performing an exhaustive search. For the experimental analysis, different SAR images of urban, forest and agricultural scenes and two different US images of a human heart have been used. Simulation results show the efficiency of the proposed method in finding statistical models for speckle.

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Section: Gamma Distribution Familymentioning

confidence: 99%

Generalized Bayesian Model Selection for Speckle on Remote Sensing Images

Karakuş

Kuruoğlu

Altınkaya

2019

IEEE Trans. on Image Process.

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“…In order to obtain accurate change detection results, a more appropriate distribution model should be adopted and, consequently, new versions of the K&I algorithm need to be developed. Weibull and gamma distributions are widely used in analyzing the statistical characterization of SAR data [41], and they are applied in image segmentation [42], texture analysis [43], deformation modeling [44], classification [45], and target detection [46]. However, to our knowledge, Weibull or gamma distribution was not previously applied to describe the statistical behaviors of a change detection DI map.…”

Section: Introductionmentioning

confidence: 99%

A Novel Change Detection Method Based on Statistical Distribution Characteristics Using Multi-Temporal PolSAR Data

Zhao

Chang

Niu

et al. 2020

Sensors

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Unsupervised change detection approaches, which are relatively straightforward and easy to implement and interpret, and which require no human intervention, are widely used in change detection. Polarimetric synthetic aperture radar (PolSAR), which has an all-weather response capability with increased polarimetric information, is a key tool for change detection. However, for PolSAR data, inadequate evaluation of the difference image (DI) map makes the threshold-based algorithms incompatible with the true distribution model, which causes the change detection results to be ineffective and inaccurate. In this paper, to solve these problems, we focus on the generation of the DI map and the selection of the optimal threshold. An omnibus test statistic is used to generate the DI map from multi-temporal PolSAR images, and an improved Kittler and Illingworth algorithm based on either Weibull or gamma distribution is used to obtain the optimal threshold for generating the change detection map. Multi-temporal PolSAR data obtained by the Radarsat-2 sensor over Wuhan in China are used to verify the efficiency of the proposed method. The experimental results using our approach obtained the best performance in East Lake and Yanxi Lake regions with false alarm rates of 1.59% and 1.80%, total errors of 2.73% and 4.33%, overall accuracy of 97.27% and 95.67%, and Kappa coefficients of 0.6486 and 0.6275, respectively. Our results demonstrated that the proposed method is more suitable than the other compared methods for multi-temporal PolSAR data, and it can obtain both effective and accurate results.

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“…With an increase in radar's spatial resolution, the amplitude distribution of the sea clutters was further extended from the Gaussian to compound-Gaussian probability density functions (PDFs) in [8] for small target detection. Gao et al in [9] This work was supported in part by the National Science Foundation of China with Grant Numbers 61601192, 61631015, and 61729101, the Young Elite Scientists Sponsorship Program by CAST with Grant Number 2017QNRC001, the State Key Laboratory of Integrated Services Networks (Xidian University) with Grant Number ISN19-09, and the Fundamental Research Funds for the Central Universities with Grant Numbers 2016YXMS298 and 2015ZDTD012. adopted the generalized Gamma distribution to describe the statistical behaviors of sea clutters, and provided a parameter estimation scheme by taking both of estimation precision and applicable conditions into consideration.…”

Section: Introductionmentioning

confidence: 99%

Sea Clutter Distribution Modeling: A Kernel Density Estimation Approach

Zhou

Jiang

2018

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)

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An accurate sea clutter distribution is crucial for decision region determination when detecting sea-surface floating targets. However, traditional parametric models possibly have a considerable gap to the realistic distribution of sea clutters due to the volatile sea states. In this paper, we develop a kernel density estimation based framework to model the sea clutter distributions without requiring any prior knowledge. In this framework, we jointly consider two embedded fundamental problems, the selection of a proper kernel density function and the determination of its corresponding optimal bandwidth. Regarding these two problems, we adopt the Gaussian, Gamma, and Weibull distributions as the kernel functions, and derive the closed-form optimal bandwidth equations for them. To deal with the highly complicated equations for the three kernels, we further design a fast iterative bandwidth selection algorithm to solve them. Experimental results show that, compared with existing methods, our proposed approach can significantly decrease the error incurred by sea clutter modeling (about two orders of magnitude reduction) and improve the target detection probability (up to 36% in low false alarm rate cases).

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Scheme of Parameter Estimation for Generalized Gamma Distribution and Its Application to Ship Detection in SAR Images

Cited by 93 publications

References 57 publications

Generalized Bayesian Model Selection for Speckle on Remote Sensing Images

Generalized Bayesian Model Selection for Speckle on Remote Sensing Images

A Novel Change Detection Method Based on Statistical Distribution Characteristics Using Multi-Temporal PolSAR Data

Sea Clutter Distribution Modeling: A Kernel Density Estimation Approach

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