Parameter Estimation of Generalized Gamma Distribution Toward SAR Image Processing

Zhang, Peng; Li, Beibei; Boudaren, Mohamed El Yazid; Yan, Jian Hua; Wu, Yan

doi:10.1109/taes.2020.2977790

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…However, many issues faced in the real world are often much more complicated than single-mode problems. A typical example is system noise, including process noise and observation noise, which is not a Gaussian distribution, but a gamma distribution or other complex mixed distributions [ 19 ]. Therefore, with the filtering of the above mentioned algorithms based on the Gaussian noise assumption, it is difficult to obtain a satisfactory estimation performance due to the model mismatch.…”

Section: Introductionmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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Section: Introductionmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…There are some statistical models that fit the distribution of gray levels in SAR images; these include Gamma distribution, Gamma mixture distribution (GMD), generalized Gamma distribution (GGD), etc. [15][16][17][18][19][20][21][22]. The gray levels of the 3-D SAR images that we processed [13,23,24] follow Gamma distribution.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Region of Interest Extraction Algorithm with Adaptive Threshold for 3-D Synthetic Aperture Radar Images

Tian

Wang

et al. 2021

Remote Sensing

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Most of the existing image segmentation methods have a strong anti-noise ability but are susceptible to the interference in the background, so they are not suitable for 3-D synthetic aperture radar (SAR) image target extraction. Region of interest (ROI) extraction can improve the anti-interference ability of the image segmentation methods. However, the existing ROI extraction method uses the same threshold to process all the images in the data set. This threshold is not optimal for each image. Designed for 3-D SAR image target extraction, we propose an ROI extraction algorithm with adaptive threshold (REAT) to enhance the anti-interference ability of the existing image segmentation methods. The required thresholds in the proposed algorithm are adaptively obtained by the mapping of the image features. Moreover, the proposed algorithm can easily be applied to existing image segmentation methods. The experiments demonstrate that the proposed algorithm significantly enhances the anti-interference ability and computational efficiency of the image segmentation methods. Compared with the existing ROI extraction algorithm, the proposed algorithm improves the dice similarity coefficient by 6.4%.

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