scholarly journals Bayesian angular super-resolution for sea-surface target in forward-looking scanning radar

2019 ◽  
Vol 2019 (21) ◽  
pp. 7783-7786
Author(s):  
Changlin Li ◽  
Yin Zhang ◽  
Deqing Mao ◽  
Yunlin Huang ◽  
Jianyu Yang
Keyword(s):  
Super Resolution ◽  
Sea Surface ◽  
Forward Looking

scholarly journals Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method

2021 ◽  
Vol 14 (1) ◽  
pp. 26
Author(s):  
Weixin Li ◽  
Ming Li ◽  
Lei Zuo ◽  
Hao Sun ◽  
Hongmeng Chen ◽  
...  
Keyword(s):  
Latent Variable ◽  
Bayesian Method ◽  
Likelihood Function ◽  
Super Resolution ◽  
Gaussian Mixture ◽  
Sea Surface ◽  
Ground Clutter ◽  
Em Method ◽  
Contour Information ◽  
Forward Looking

Traditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour information of the target has an important effect, for example, in the autonomous landing on a ship. This paper aims to realize the forward-looking imaging of a sea-surface target. In this paper, a multi-prior Bayesian method, which considers the environment and fuses the contour information and the sparsity of the sea-surface target, is proposed. Firstly, due to the imaging environment in which more than one kind of clutter exists, we introduce the Gaussian mixture model (GMM) as the prior information to describe the interference of the clutter and noise. Secondly, we fuse the total variation (TV) prior and Laplace prior, and propose a multi-prior to model the contour information and sparsity of the target. Third, we introduce the latent variable to simplify the logarithm likelihood function. Finally, to solve the optimal parameters, the maximum posterior-expectation maximization (MAP-EM) method is utilized. Experimental results illustrate that the multi-prior Bayesian method can enhance the azimuth resolution, and preserve the contour information of the sea-surface target.

Bayesian Azimuth Super-resolution of Sea-surface Target in Forward-looking Imaging

2020 ◽  
Author(s):  
Yao Kang ◽  
Yin Zhang ◽  
Deqing Mao ◽  
Xingyu Tuo ◽  
Yulin Huang ◽  
...  
Keyword(s):  
Super Resolution ◽  
Sea Surface ◽  
Forward Looking

Efficient downscaling of satellite oceanographic data with convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 46-47
Author(s):  
Nikita Saxena
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Sea Surface ◽  
Clear Sky ◽  
Oceanographic Data ◽  
Computationally Expensive ◽  
Sky Conditions ◽  
Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

scholarly journals A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

2021 ◽  
Vol 13 (20) ◽  
pp. 4115
Author(s):  
Ke Tan ◽  
Xingyu Lu ◽  
Jianchao Yang ◽  
Weimin Su ◽  
Hong Gu
Keyword(s):  
Random Field ◽  
Objective Function ◽  
Markov Random Field ◽  
Prior Information ◽  
Structural Characteristics ◽  
Super Resolution ◽  
Resolution Method ◽  
Markov Random ◽  
Shrinkage Method ◽  
Forward Looking

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.

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