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.

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

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

scholarly journals Expectation-maximization analysis of spatial time series

2007 ◽  
Vol 14 (1) ◽  
pp. 73-77 ◽  
Author(s):  
K. W. Smith ◽  
A. L. Aretxabaleta
Keyword(s):  
Time Series ◽  
Expectation Maximization ◽  
Time Series Data ◽  
Gaussian Mixture ◽  
Sea Surface ◽  
Series Data ◽  
Eof Analysis ◽  
Orthogonal Function ◽  
The Em Algorithm ◽  
Em Method

Abstract. Expectation maximization (EM) is used to estimate the parameters of a Gaussian Mixture Model for spatial time series data. The method is presented as an alternative and complement to Empirical Orthogonal Function (EOF) analysis. The resulting weights, associating time points with component distributions, are used to distinguish physical regimes. The method is applied to equatorial Pacific sea surface temperature data from the TAO/TRITON mooring time series. Effectively, the EM algorithm partitions the time series into El Niño, La Niña and normal conditions. The EM method leads to a clearer interpretation of the variability associated with each regime than the basic EOF analysis.

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 Reconciling differences in stratospheric ozone composites

2017 ◽  
Vol 17 (20) ◽  
pp. 12269-12302 ◽  
Author(s):  
William T. Ball ◽  
Justin Alsing ◽  
Daniel J. Mortlock ◽  
Eugene V. Rozanov ◽  
Fiona Tummon ◽  
...  
Keyword(s):  
Time Series ◽  
Likelihood Function ◽  
Stratospheric Ozone ◽  
Gaussian Mixture ◽  
Robust Estimate ◽  
Mixture Density ◽  
Source Data ◽  
Ozone Trends ◽  
Decadal Trend

Abstract. Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10° bands from 60° S to 60° N and from 46 to 1 hPa (∼ 21–48 km) for 1985–2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems – we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.

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