scholarly journals A Comparison of Optimized Sentinel-2 Super-Resolution Methods Using Wald’s Protocol and Bayesian Optimization

2021 ◽  
Vol 13 (11) ◽  
pp. 2192
Author(s):  
Sveinn E. Armannsson ◽  
Magnus O. Ulfarsson ◽  
Jakob Sigurdsson ◽  
Han V. Nguyen ◽  
Johannes R. Sveinsson
Keyword(s):  
Synthetic Data ◽  
Super Resolution ◽  
Earth Observation ◽  
Bayesian Optimization ◽  
Multispectral Images ◽  
Regression Kriging ◽  
Tuning Parameters ◽  
Quantitative Results ◽  
Reduced Scale ◽  
Sentinel 2

In the context of earth observation and remote sensing, super-resolution aims to enhance the resolution of a captured image by upscaling and enhancing its details. In recent years, numerous methods for super-resolution of Sentinel-2 (S2) multispectral images have been suggested. Most of those methods depend on various tuning parameters that affect how effective they are. This paper’s aim is twofold. Firstly, we propose to use Bayesian optimization at a reduced scale to select tuning parameters. Secondly, we choose tuning parameters for eight S2 super-resolution methods and compare them using real and synthetic data. While all the methods give good quantitative results, Area-To-Point Regression Kriging (ATPRK), Sentinel-2 Sharpening (S2Sharp), and Sentinel-2 Symmetric Skip Connection convolutional neural network (S2 SSC) perform markedly better on several datasets than the other methods tested in this paper.

BEWATS: BEACH WASTE TRACKING SYSTEM USING SATELLITE, UAV’s and MARINE DYNAMICS MODELS. 

2021 ◽  
Author(s):  
Omjyoti Dutta ◽  
Beatriz Revilla-Romero ◽  
Adrian Sanz-Díaz ◽  
Fernando Martin-Rodriguez ◽  
Orentino Mojon-Ojea ◽  
...  
Keyword(s):  
Industrial Development ◽  
Tracking System ◽  
Synthetic Data ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
Marine Litter ◽  
North West ◽  
Area Of Interest ◽  
Dynamics Models ◽  
Sentinel 2

<p>Marine litter is a growing problem that advances parallel to economic and industrial development and seriously affects ecosystems. One of the most abundant pollutants are plastics. The BEWATS project focuses on innovative tools for remote marine litter control and management through satellite and UAV’s. The areas of study are currently at the Vigo coast in Galicia (North-West of Spain). In this area, there are many high natural value beaches including Nature Reserve and part of a National Park. These beaches are receiving an increasing amount of marine litter, mainly plastic, helped by strong currents in the area. Every few months, these beaches are clean and the collected litter information tracked. In this context, the BEWATS project concentrates on tracking the possible path through which marine litter reaches the area of interest. In this presentation, we will discuss how this is achieved by data fusion from UAV imagery, marine dynamics model simulations and Earth-observation satellite data (Sentinel-2). To detect possible marine litter, we have developed a novel synthetic data-based approach to marine litter detection using Sentinel-2 images and machine learning techniques. Within this approach, one can classify and quantify according to pixel-level litter fraction present. We have validated our approach with existing open-sourced available datasets.  </p><p>The BEWATS project is led by Vigo University, which provides UAV’s imagery, and the Spanish Research Council (CSIC) provides marine dynamics models for tracking waste routes and delineation of waste concentration zones. In this context, GMV provides Earth observation based solution of detecting marine litter. BEWATS is founded by the Biodiversity Foundation of the Spanish Ministry for the Ecological Transition and the Demographic Challenge.</p>

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

SISSI Project: A Feasibility Study for a Super Resolved Compressive Sensing Multispectral Imager in the Medium Infrared

2021 ◽  
Vol 8 (1) ◽  
pp. 28
Author(s):  
Cinzia Lastri ◽  
Gabriele Amato ◽  
Massimo Baldi ◽  
Tiziano Bianchi ◽  
Maria Fabrizia Buongiorno ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Compressive Sensing ◽  
Feasibility Study ◽  
Spatial Resolution ◽  
Spatial Light Modulator ◽  
Super Resolution ◽  
Earth Observation ◽  
Light Modulator ◽  
The Core ◽  
Multispectral Imager

This paper describes the activities related to a feasibility study for an Earth observation optical payload, operating in the medium infrared, based on super-resolution and compressive sensing techniques. The presented activities are running in the framework of the ASI project SISSI, aiming to improve ground spatial resolution and mitigate saturation/blooming effects. The core of the payload is a spatial light modulator (SLM): a bidimensional array of micromirrors electronically actuated. Thanks to compressive sensing approach, the proposed payload eliminates the compression board, saving mass, memory and energy consumption.

EDPANs: Enhanced Dual Path Attention Networks for Single Image Super-Resolution

2021 ◽  
pp. 2150300
Author(s):  
Guoan Cheng ◽  
Ai Matsune ◽  
Huaijuan Zang ◽  
Toru Kurihara ◽  
Shu Zhan
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Single Image ◽  
Residual Network ◽  
Dense Structure ◽  
Attention Networks ◽  
Residual Structure ◽  
Quantitative Results ◽  
Image Super Resolution ◽  
Single Image Super Resolution

In this paper, we propose an enhanced dual path attention network (EDPAN) for image super-resolution. ResNet is good at implicitly reusing extracted features, DenseNet is good at exploring new features. Dual Path Network (DPN) combines ResNets and DenseNet to create a more accurate architecture than the straightforward one. We experimentally show that the residual network performs best when each block consists of two convolutions, and the dense network performs best when each micro-block consists of one convolution. Following these ideas, our EDPAN exploits the advantages of the residual structure and the dense structure. Besides, to deploy the computations for features more effectively, we introduce the attention mechanism into our EDPAN. Moreover, to relieve the parameters burden, we also utilize recursive learning to propose a lightweight model. In the experiments, we demonstrate the effectiveness and robustness of our proposed EDPAN on different degradation situations. The quantitative results and visualization comparison can sufficiently indicate that our EDPAN achieves favorable performance over the state-of-the-art frameworks.

scholarly journals Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network

2018 ◽  
Vol 146 ◽  
pp. 305-319 ◽  
Author(s):  
Charis Lanaras ◽  
José Bioucas-Dias ◽  
Silvano Galliani ◽  
Emmanuel Baltsavias ◽  
Konrad Schindler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Sentinel 2

scholarly journals Bayesian optimization for tuning chaotic systems

2014 ◽  
Vol 1 (2) ◽  
pp. 1283-1312
Author(s):  
M. Abbas ◽  
A. Ilin ◽  
A. Solonen ◽  
J. Hakkarainen ◽  
E. Oja ◽  
...  
Keyword(s):  
Chaotic Systems ◽  
Climate Models ◽  
Atmospheric Model ◽  
Bayesian Optimization ◽  
Tuning Parameters ◽  
Performance Metric ◽  
Weather And Climate ◽  
Computationally Expensive ◽  
Low Dimensional ◽  
Complex Chaotic Systems

Abstract. In this work, we consider the Bayesian optimization (BO) approach for tuning parameters of complex chaotic systems. Such problems arise, for instance, in tuning the sub-grid scale parameterizations in weather and climate models. For such problems, the tuning procedure is generally based on a performance metric which measures how well the tuned model fits the data. This tuning is often a computationally expensive task. We show that BO, as a tool for finding the extrema of computationally expensive objective functions, is suitable for such tuning tasks. In the experiments, we consider tuning parameters of two systems: a simplified atmospheric model and a low-dimensional chaotic system. We show that BO is able to tune parameters of both the systems with a low number of objective function evaluations and without the need of any gradient information.

scholarly journals A Sentinel-2 Dataset for Uganda

Data ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 35
Author(s):  
Jonas Ardö
Keyword(s):  
Environmental Monitoring ◽  
Input Data ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Output Data ◽  
Radiometric Correction ◽  
Clear Sky ◽  
Earth Observation Data ◽  
Sentinel 2

Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.

scholarly journals A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 9-14
Author(s):  
F. Pineda ◽  
V. Ayma ◽  
C. Beltran
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Structural Similarity ◽  
Generative Adversarial Network ◽  
Acceptable Result ◽  
Adversarial Network ◽  
High Resolution Satellite Images ◽  
Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

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