90 research outputs found
Can We "Sense" the Call of The Ocean? Current Advances in Remote Sensing Computational Imaging for Marine Debris Monitoring
Especially due to the unconscious use of petroleum products, the ocean faces
a potential danger: . Plastic pollutes not only the
ocean but also directly the air and foods whilst endangering the ocean
wild-life due to the ingestion and entanglements. Especially, during the last
decade, public initiatives and academic institutions have spent an enormous
time on finding possible solutions to marine plastic pollution. Remote sensing
imagery sits in a crucial place for these efforts since it provides highly
informative earth observation products. Despite this, detection, and monitoring
of the marine environment in the context of plastic pollution is still in its
early stages and the current technology offers possible important development
for the computational efforts. This paper contributes to the literature with a
thorough and rich review and aims to highlight notable literature milestones in
marine debris monitoring applications by promoting the computational imaging
methodology behind these approaches.Comment: 25 pages, 11 figure
Ship Wake Detection in SAR Images via Sparse Regularization
In order to analyse synthetic aperture radar (SAR) images of the sea surface,
ship wake detection is essential for extracting information on the wake
generating vessels. One possibility is to assume a linear model for wakes, in
which case detection approaches are based on transforms such as Radon and
Hough. These express the bright (dark) lines as peak (trough) points in the
transform domain. In this paper, ship wake detection is posed as an inverse
problem, which the associated cost function including a sparsity enforcing
penalty, i.e. the generalized minimax concave (GMC) function. Despite being a
non-convex regularizer, the GMC penalty enforces the overall cost function to
be convex. The proposed solution is based on a Bayesian formulation, whereby
the point estimates are recovered using maximum a posteriori (MAP) estimation.
To quantify the performance of the proposed method, various types of SAR images
are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The
performance of various priors in solving the proposed inverse problem is first
studied by investigating the GMC along with the L1, Lp, nuclear and total
variation (TV) norms. We show that the GMC achieves the best results and we
subsequently study the merits of the corresponding method in comparison to two
state-of-the-art approaches for ship wake detection. The results show that our
proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page
Measuring Air Quality via Multimodal AI and Satellite Imagery
Climate change may be classified as the most important environmental problem
that the Earth is currently facing, and affects all living species on Earth.
Given that air-quality monitoring stations are typically ground-based their
abilities to detect pollutant distributions are often restricted to wide areas.
Satellites however have the potential for studying the atmosphere at large; the
European Space Agency (ESA) Copernicus project satellite, "Sentinel-5P" is a
newly launched satellite capable of measuring a variety of pollutant
information with publicly available data outputs. This paper seeks to create a
multi-modal machine learning model for predicting air-quality metrics where
monitoring stations do not exist. The inputs of this model will include a
fusion of ground measurements and satellite data with the goal of highlighting
pollutant distribution and motivating change in societal and industrial
behaviors. A new dataset of European pollution monitoring station measurements
is created with features including from
the ESA Copernicus project. This dataset is used to train a multi-modal ML
model, Air Quality Network (AQNet) capable of fusing these various types of
data sources to output predictions of various pollutants. These predictions are
then aggregated to create an "air-quality index" that could be used to compare
air quality over different regions. Three pollutants, NO, O, and
PM, are predicted successfully by AQNet and the network was found to be
useful compared to a model only using satellite imagery. It was also found that
the addition of supporting data improves predictions. When testing the
developed AQNet on out-of-sample data of the UK and Ireland, we obtain
satisfactory estimates though on average pollution metrics were roughly
overestimated by around 20\%.Comment: 14 pages, 9 figures, 4 table
Sentiment Analysis for Measuring Hope and Fear from Reddit Posts During the 2022 Russo-Ukrainian Conflict
This paper proposes a novel lexicon-based unsupervised sentimental analysis
method to measure the and for the 2022
Ukrainian-Russian Conflict. is utilised as the main
source of human reactions to daily events during nearly the first three months
of the conflict. The top 50 posts of six different subreddits about
Ukraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict,
UkraineWarVideoReport, UkraineWarReports) and their relative comments are
scraped and a data set is created. On this corpus, multiple analyses such as
(1) public interest, (2) hope/fear score, (3) stock price interaction are
employed. We promote using a dictionary approach, which scores the hopefulness
of every submitted user post. The Latent Dirichlet Allocation (LDA) algorithm
of topic modelling is also utilised to understand the main issues raised by
users and what are the key talking points. Experimental analysis shows that the
hope strongly decreases after the symbolic and strategic losses of Azovstal
(Mariupol) and Severodonetsk. Spikes in hope/fear, both positives and
negatives, are present after important battles, but also some non-military
events, such as Eurovision and football games.Comment: 23 pages, 8 figures, 2 table
On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty
Synthetic aperture radar (SAR) imagery can provide useful information in a
multitude of applications, including climate change, environmental monitoring,
meteorology, high dimensional mapping, ship monitoring, or planetary
exploration. In this paper, we investigate solutions to a number of inverse
problems encountered in SAR imaging. We propose a convex proximal splitting
method for the optimization of a cost function that includes a non-convex
Cauchy-based penalty. The convergence of the overall cost function optimization
is ensured through careful selection of model parameters within a
forward-backward (FB) algorithm. The performance of the proposed penalty
function is evaluated by solving three standard SAR imaging inverse problems,
including super-resolution, image formation, and despeckling, as well as ship
wake detection for maritime applications. The proposed method is compared to
several methods employing classical penalty functions such as total variation
() and norms, and to the generalized minimax-concave (GMC) penalty.
We show that the proposed Cauchy-based penalty function leads to better image
reconstruction results when compared to the reference penalty functions for all
SAR imaging inverse problems in this paper.Comment: 18 pages, 7 figure
A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)
Football is a very result-driven industry, with goals being rarer than in
most sports, so having further parameters to judge the performance of teams and
individuals is key. Expected Goals (xG) allow further insight than just a
scoreline. To tackle the need for further analysis in football, this paper uses
machine learning applications that are developed and applied to Football Event
data. From the concept, a Binary Classification problem is created whereby a
probabilistic valuation is outputted using Logistic Regression and Gradient
Boosting based approaches. The model successfully predicts xGs probability
values for football players based on 15,575 shots. The proposed solution
utilises StatsBomb as the data provider and an industry benchmark to tune the
models in the right direction. The proposed ML solution for xG is further used
to tackle the age-old cliche of: 'the ball has fallen to the wrong guy there'.
The development of the model is used to adjust and gain more realistic values
of expected goals than the general models show. To achieve this, this paper
tackles Positional Adjusted xG, splitting the training data into Forward,
Midfield, and Defence with the aim of providing insight into player qualities
based on their positional sub-group. Positional Adjusted xG successfully
predicts and proves that more attacking players are better at accumulating xG.
The highest value belonged to Forwards followed by Midfielders and Defenders.
Finally, this study has further developments into Player Adjusted xG with the
aim of proving that Messi is statistically at a higher efficiency level than
the average footballer. This is achieved by using Messi subset samples to
quantify his qualities in comparison to the average xG models finding that
Messi xG performs 347 xG higher than the general model outcome.Comment: 16 pages, 8 tables, 6 figure
On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring
Marine plastic pollution is an emerging environmental problem since it pollutes the ocean, air and food whilst endangering the ocean wildlife via the ingestion and entanglements. During the last decade, an enormous effort has been spent on finding possible solutions to marine plastic pollution. Remote sensing imagery sits in a crucial place for these efforts since it provides informative earth observation products, and the current technology offers further essential development. Despite the advances in the last decade, there is still a way to go for marine plastic monitoring research where challenges are rarely highlighted. This paper contributes to the literature with a critical review and aims to highlight literature milestones in marine debris and suspected plastics (MD&SP) monitoring by promoting the computational imaging methodology behind these approaches along with detailed discussions on challenges and potential future research directions
Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation
Ship wake detection is of great importance in the characterisation of
synthetic aperture radar (SAR) images of the ocean surface since wakes usually
carry essential information about vessels. Most detection methods exploit the
linear characteristics of the ship wakes and transform the lines in the spatial
domain into bright or dark points in a transform domain, such as the Radon or
Hough transforms. This paper proposes an innovative ship wake detection method
based on sparse regularisation to obtain the Radon transform of the SAR image,
in which the linear features are enhanced. The corresponding cost function
utilizes the Cauchy prior, and on this basis, the Cauchy proximal operator is
proposed. A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm
(MYULA), which is computationally efficient and robust is used to estimate the
image in the transform domain by minimizing the negative log-posterior
distribution. The detection accuracy of the Cauchy prior based approach is
86.7%, which is demonstrated by experiments over six COSMO-SkyMed images.Comment: 9 pages, 2 Figures and 2 Table
A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface
The analysis of ocean surface is widely performed using synthetic aperture
radar (SAR) imagery as it yields information for wide areas under challenging
weather conditions, during day or night, etc. Speckle noise constitutes however
the main reason for reduced performance in applications such as classification,
ship detection, target tracking and so on. This paper presents an investigation
into the despeckling of SAR images of the ocean that include ship wake
structures, via sparse regularisation using the Cauchy proximal operator. We
propose a closed-form expression for calculating the proximal operator for the
Cauchy prior, which makes it applicable in generic proximal splitting
algorithms. In our experiments, we simulate SAR images of moving vessels and
their wakes. The performance of the proposed method is evaluated in comparison
to the L1 and TV norm regularisation functions. The results show a superior
performance of the proposed method for all the utilised images generated.Comment: 6 pages, 2 Figures. This work has been presented in IGARSS 202
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