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Machine Learning for Cloud Detection of Globally Distributed Sentinel-2 Images

Roberto Cilli, Alfonso Monaco, Nicola Amoroso, Andrea Tateo, Sabina Tangaro, Roberto Bellotti
2020 Remote Sensing  
In recent years, a number of different procedures have been proposed for segmentation of remote sensing images, basing on spectral information.  ...  We used a training set for learning and two different independent sets for testing. The comparison accounted for 135 images acquired from 54 different worldwide sites.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12152355 fatcat:hzsezy25zfekpmjqqf47zrrrqy

Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests

Ana María Pacheco-Pascagaza, Yaqing Gou, Valentin Louis, John F. Roberts, Pedro Rodríguez-Veiga, Polyanna da Conceição Bispo, Fernando D. B. Espírito-Santo, Ciaran Robb, Caroline Upton, Gustavo Galindo, Edersson Cabrera, Indira Paola Pachón Cendales (+5 others)
2022 Remote Sensing  
The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly  ...  The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution.  ...  Also supported by EASOS and the National Centre for Earth Observation (NCEO). Forest Sentinel was supported by NERC "REDD+ Monitoring Services with Satellite Earth Observation" (NE/N017021/1).  ... 
doi:10.3390/rs14030707 fatcat:te4pw4shubd7xolqp2y4o76xui

TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITH LEARNED SPATIAL FEATURES USING SENTINEL 2

J. Mifdal, N. Longépé, M. Rußwurm
2021 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper, we focus on detecting big patches of floating objects that can contain plastic as well as other materials with optical Sentinel 2 data.  ...  Along with this work, we provide a hand-labeled Sentinel 2 dataset of floating objects on the sea surface and other water bodies such as lakes together with pre-trained deep learning models.  ...  The Sentinel 2 data is provided following two-level of processing: L1C top-of-atmosphere and L2A bottom-ofatmosphere. The L1C data has 13 bands including one band for clouds detection.  ... 
doi:10.5194/isprs-annals-v-3-2021-285-2021 fatcat:437ed3xxhrgyzbgspk2ztwqlg4

Sentinel-2 Data for Land Cover/Use Mapping: A Review

Darius Phiri, Matamyo Simwanda, Serajis Salekin, Vincent R. Nyirenda, Yuji Murayama, Manjula Ranagalage
2020 Remote Sensing  
The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF).  ...  Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images.  ...  Acknowledgments: We would like to acknowledge the anonymous reviewers for their valuable suggestions that have helped to improve this manuscript as they worked tirelessly during the difficult time of COVID  ... 
doi:10.3390/rs12142291 fatcat:3lifmassi5avbf2gexwvb4pg6a

OIL PALM PLANTATION DETECTION IN INDONESIA USING SENTINEL-2 AND LANDSAT-8 OPTICAL SATELLITE IMAGERY (CASE STUDY: ROKAN HULU REGENCY, RIAU PROVINCE)

Yunita Nurmasari, Arie Wahyu Wijayanto
2021 International Journal of Remote Sensing and Earth Sciences (IJReSES)  
The objective of this work is to assess the capability of multispectral optical Landsat and Sentinel images to detect oil palm plantations in Rokan Hulu, Riau, one of the largest palm oil producers in  ...  The findings and contributions of the study include: (1) insight into a set of feature combinations that provides the highest model accuracy, and (2) an extensive evaluation of machine learning-based classifiers  ...  All authors have read and agreed to the published version of the manuscript.  ... 
doi:10.30536/j.ijreses.2021.v18.a3537 fatcat:hfitnjze2ngr5dwptyumuyrc5u

KappaMask: AI-Based Cloudmask Processor for Sentinel-2

Marharyta Domnich, Indrek Sünter, Heido Trofimov, Olga Wold, Fariha Harun, Anton Kostiukhin, Mihkel Järveoja, Mihkel Veske, Tanel Tamm, Kaupo Voormansik, Aire Olesk, Valentina Boccia (+2 others)
2021 Remote Sensing  
For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled.  ...  KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products.  ...  We also thank Gholamreza Anbarjafari at the University of Tartu for the feedback and directions. The project was funded by European Space Agency, Contract No. 4000132124/20/I-DT.  ... 
doi:10.3390/rs13204100 fatcat:vbkdrjovkrbqje4np44pof63om

Detection of Flood Extent Using Sentinel-1A/B Synthetic Aperture Radar: An Application for Hurricane Harvey, Houston, TX

Kristy F. Tiampo, Lingcao Huang, Conor Simmons, Clay Woods, Margaret T. Glasscoe
2022 Remote Sensing  
We implement two applications with SAR GRD data, an amplitude thresholding technique applied, for the first time, to Sentinel-1A/B SAR data, and a machine learning technique, DeepLabv3+.  ...  Here, we present a comparison of several methods for characterizing flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods.  ...  Machine Learning and DeepLabV3+ The goal of this phase of the project is to train a machine learning model to detect differences between pixels in our SAR scenes and attribute these changes to inundation  ... 
doi:10.3390/rs14092261 fatcat:jvyluf36eve5lgp3if2kbr3jnm

Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach

Kashyap Raiyani, Teresa Gonçalves, Luís Rato, Pedro Salgueiro, José R. Marques da Silva
2021 Remote Sensing  
A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other).  ...  Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance  ...  Figure 1 . 1 Sen2cor Cloud and Snow Mask Algorithm. Figure 2 . 2 Generation of the Extended Database for Machine Learning (ML) and Sen2Cor Assessment.  ... 
doi:10.3390/rs13020300 fatcat:qmdtmqzeiverlbsuyrad5g6mtm

Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images

André Hollstein, Karl Segl, Luis Guanter, Maximilian Brell, Marta Enesco
2016 Remote Sensing  
The Multi-Spectral Imager on board the Sentinel-2's of the Copernicus program offers optimized bands for this task and delivers unprecedented amounts of data regarding spatial sampling, global coverage  ...  Classification of clouds, cirrus, snow, shadows and clear sky areas is a crucial step in the pre-processing of optical remote sensing images and is a valuable input for their atmospheric correction.  ...  Marta Enesco was responsible for the manual classification of Sentinel-2 data. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs8080666 fatcat:csi6oa6yrzad5hvqwvpql22vzy

Index [chapter]

2021 Earth Observation for Flood Applications  
chain, workflow of, 19f Sentinel-1 Flood Service, 8, 16 Sentinel-2, 8 Sentinel-2 images showing visual differences during a flood event, 132f Sentinel-2/Landsat flood processing chain, Workflow  ...  See Deep learning (DL) methods DL algorithms, 229 DLR's Sentinel-2, 11 DN.  ... 
doi:10.1016/b978-0-12-819412-6.00021-3 fatcat:a7v6p3ku3jbdtf5awcluvleqby

2020 Index IEEE Transactions on Big Data Vol. 6

2021 IEEE Transactions on Big Data  
., +, TBData June 2020 359-368 Comparison of Different Machine Learning Approaches to Predict Small for Gestational Age Infants.  ...  ., +, TBData Sept. 2020 507-521 Image segmentation Mosaicking Copernicus Sentinel-1 Data at Global Scale.  ... 
doi:10.1109/tbdata.2021.3055661 fatcat:umqhfarwlbeclhgdvukhf25xbe

Towards global flood mapping onboard low cost satellites with machine learning

Gonzalo Mateo-Garcia, Joshua Veitch-Michaelis, Lewis Smith, Silviu Vlad Oprea, Guy Schumann, Yarin Gal, Atılım Güneş Baydin, Dietmar Backes
2021 Scientific Reports  
The ESA's recent PhiSat-1 mission aims to facilitate the demonstration of this concept, providing the hardware capability to perform onboard processing by including a power-constrained machine learning  ...  Onboard processing offers a solution to decrease the amount of data to transmit by reducing large sensor images to smaller data products.  ...  The dataset contains pairs of Sentinel-2 images and flood extent maps covering 119 global flood events. 2.  ... 
doi:10.1038/s41598-021-86650-z pmid:33790368 fatcat:5nk3hwsu2ve4plejnvddlvyrbu

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Xue Liu, Temilola E. Fatoyinbo, Nathan M. Thomas, Weihe Wendy Guan, Yanni Zhan, Pinki Mondal, David Lagomasino, Marc Simard, Carl C. Trettin, Rinki Deo, Abigail Barenblitt
2021 Frontiers in Earth Science  
The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel  ...  The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data.  ...  As all reference samples, Sentinel-2, and Sentinel-1 images were linked geographically, for each of the sample sites, the corresponding pixel values on Sentinel-2 and Sentinel-1 images were extracted as  ... 
doi:10.3389/feart.2020.560933 fatcat:d4cdam77dndx7dw4sxw52mhd3e

A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images

Xidong Chen, Liangyun Liu, Yuan Gao, Xiao Zhang, Shuai Xie
2020 Remote Sensing  
The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively  ...  The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with  ...  cloud detection approaches: the rule-based approach and the operational single classifier-based machine learning approach (classify images using a single global classifier) ( Figure 10 ).  ... 
doi:10.3390/rs12152365 fatcat:lybmyq56infxtdolcex2yvnhni

Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas

Dimosthenis Traganos, Bharat Aggarwal, Dimitris Poursanidis, Konstantinos Topouzelis, Nektarios Chrysoulakis, Peter Reinartz
2018 Remote Sensing  
Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop  ...  the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution.  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/rs10081227 fatcat:dsiibq5erjfpbknsiu7apn7roe
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