scholarly journals Extracting multilayer networks from Sentinel-2 satellite image time series

2020 ◽  
Vol 8 (S1) ◽  
pp. S26-S42 ◽  
Author(s):  
Roberto Interdonato ◽  
Raffaele Gaetano ◽  
Danny Lo Seen ◽  
Mathieu Roche ◽  
Giuseppe Scarpa
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Image ◽  
European Space Agency ◽  
Image Data ◽  
Earth Observation ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Case Scenario ◽  
Sentinel 2

AbstractNowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

scholarly journals Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Agronomy ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 641 ◽  
Author(s):  
Joel Segarra ◽  
Maria Luisa Buchaillot ◽  
Jose Luis Araus ◽  
Shawn C. Kefauver
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Satellite Image ◽  
European Space Agency ◽  
Stress Monitoring ◽  
Biotic Stresses ◽  
Space Agency ◽  
Wide Range ◽  
Technical Features ◽  
Sentinel 2

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

Deep learning based F-Mask alternative for Sentinel-2 images in polar regions

2021 ◽  
Author(s):  
Thorsten Seehaus ◽  
Kamal Nambiar Gopikrishnan ◽  
Veniamin Morgenshtern ◽  
Philipp Hochreuther ◽  
Matthias Braun
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Satellite Image ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Polar Regions ◽  
Unseen Data ◽  
Sentinel 2

<div>Screening clouds, cloud shadows, and snow is a critical pre-processing step that needs to be performed before any meaningful analysis can be done on satellite image data. The state of the art 'F-Mask' algorithm, which is based on multiple pixel-level threshold tests, segments the image into clear land, cloud, cloud shadow, snow, and water classes. However, we observe that the results of this algorithm are not very accurate in polar and tundra regions. The unavailability of labeled Sentinel-2 training datasets with these classes makes the traditional supervised machine learning techniques difficult to implement. Experiments with large, noisy training data on standard deep learning classification tasks like CIFAR-10 and ImageNet have shown neural networks learn clean labels faster than noisy labels. </div><div>We present a multi-level self-learning approach that trains a model to perform semantic segmentation on Sentinel-2 L1C images. We use a large dataset with labels annotated using the F-mask algorithm for the training, and a small human-labeled dataset for validation. The validation dataset contains numerous examples where the F-mask classification would have given incorrect labels. At the first step, a deep neural network with a modified U-Net architecture is trained using a dataset automatically labeled with the F-mask algorithm. The performance on the validation dataset is used to select the best model from the step, which would then be used to generate more training labels from previously unseen data. In each of the subsequent steps, a new model is trained using the labels generated using the model from the previous step. The amount of data used for training increases with each step and the application of techniques like data augmentation and dropout improves the generalization of the trained model. We show that the final model from our approach can outperform its teacher, i.e. F-Mask algorithm. </div>

scholarly journals PENGENALAN CITRA SATELIT SENTINEL-2 UNTUK PEMETAAN KELAUTAN

OSEANA ◽  
2017 ◽  
Vol 42 (3) ◽  
pp. 40-55
Author(s):  
Nadya Oktaviani ◽  
Hollanda A Kusuma
Keyword(s):  
Spatial Resolution ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
Image Data ◽  
Processing Parameters ◽  
Cloud Detection ◽  
Space Agency ◽  
Thin Cloud ◽  
Sentinel 2

RECOGNITION AND UTILIZATION OF SATELLITE IMAGE SENTINEL-2 FOR MARINE MAPPING. Sentinel-2 is a satellite launched by a collaboration between The European Commission and the European Space Agency in the Global Monitoring for Environment and Security (GMES) program. The satellite has a mission to scan the Earth’s surface simultaneously at an angle of 180 each satellite with a 5-day temporal resolution with the same appearance on the equator and has a spatial resolution of 10 m, 20 m, and 60 m. There are 13 multispectral channels including VNIR and SWIR. Four channels with 10 m spatial resolution adapt with SPOT 4/5 and user’s comply requirements for land cover classification. Six channels with 20 m spatial resolution becomes a requirement for other Level 2 processing parameters. Channels with 60 m spatial resolution are specified for atmospheric correction and cloud filtering (443 nm for aerosols, 940 nm for moisture, and 1375 for thin cloud detection). Based on these specifications, Sentinel-2 can be an alternative for users to obtain image data with spatial, temporal, radiometric, and spectral resolution is better than SPOT and Landsat. Sentinel-2 can be downloaded for free and easy by the general public. The existence of image by Sentinel-2 is expected to be used optimally, especially for remote sensing analysis in marine field.

scholarly journals APPLICATION OF REMOTE SENSING IN GEOLOGICAL MAPPING, CASE STUDY Al MAGHRABAH AREA – HAJJAH REGION, YEMEN

2017 ◽  
Vol IV-4/W4 ◽  
pp. 63-71 ◽  
Author(s):  
F. Al-Nahmi ◽  
O. Saddiqi ◽  
A. Hilali ◽  
H. Rhinane ◽  
L. Baidder ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Volcanic Rocks ◽  
Image Data ◽  
Good Method ◽  
Geological Mapping ◽  
Computational Technique ◽  
Lithological Mapping ◽  
Sentinel 2 ◽  
Optimum Index Factor

Remote sensing technology plays an important role today in the geological survey, mapping, analysis and interpretation, which provides a unique opportunity to investigate the geological characteristics of the remote areas of the earth's surface without the need to gain access to an area on the ground. The aim of this study is achievement a geological map of the study area. The data utilizes is Sentinel-2 imagery, the processes used in this study, the OIF Optimum Index Factor is a statistic value that can be used to select the optimum combination of three bands in a satellite image. It’s based on the total variance within bands and correlation coefficient between bands, ICA Independent component analysis (3, 4, 6) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals, MNF Minimum Noise Fraction (1, 2, 3) is used to determine the inherent dimensionality of image data to segregate noise in the data and to reduce the computational requirements for subsequent processing, Optimum Index Factor is a good method for choosing the best band for lithological mapping. ICA, MNF, also a practical way to extract the structural geology maps. The results in this paper indicate that, the studied area can be divided into four main geological units: Basement rocks (Meta volcanic, Meta sediments), Sedimentary rocks, Intrusive rocks, volcanic rocks. The method used in this study offers great potential for lithological mapping, by using Sentinel-2 imagery, the results were compared with existing geologic maps and were superior and could be used to update the existing maps.

scholarly journals Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

2019 ◽  
Author(s):  
Andrei Stoian ◽  
Vincent Poulain ◽  
Jordi Inglada ◽  
Victor Poughon ◽  
Dawa Derksen
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Satellite Image ◽  
Image Data ◽  
Design Decision ◽  
Land Cover Maps ◽  
Sentinel 2

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

scholarly journals Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

2019 ◽  
Author(s):  
Andrei Stoian ◽  
Vincent Poulain ◽  
Jordi Inglada ◽  
Victor Poughon ◽  
Dawa Derksen
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Satellite Image ◽  
Image Data ◽  
Design Decision ◽  
Land Cover Maps ◽  
Sentinel 2

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

scholarly journals Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 974
Author(s):  
Lorena Alves Santos ◽  
Karine Ferreira ◽  
Michelle Picoli ◽  
Gilberto Camara ◽  
Raul Zurita-Milla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Satellite Image ◽  
Spatiotemporal Patterns ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Self Organizing Maps ◽  
Machine Learning Methods ◽  
Land Use And Cover

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.

scholarly journals On the Use of Tri-Stereo Pleiades Images for the Morphometric Measurement of Dolines in the Basaltic Plateau of Azrou (Middle Atlas, Morocco)

2021 ◽  
Vol 13 (20) ◽  
pp. 4087
Author(s):  
Maria Teresa Melis ◽  
Luca Pisani ◽  
Jo De Waele
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
European Space Agency ◽  
Morphometric Study ◽  
Stereo Images ◽  
Space Agency ◽  
Middle Atlas ◽  
Mapping Techniques ◽  
Remote Sensing Techniques ◽  
Basaltic Plateau

Hundreds of large and deep collapse dolines dot the surface of the Quaternary basaltic plateau of Azrou, in the Middle Atlas of Morocco. In the absence of detailed topographic maps, the morphometric study of such a large number of features requires the use of remote sensing techniques. We present the processing, extraction, and validation of depth measurements of 89 dolines using tri-stereo Pleiades images acquired in 2018–2019 (the European Space Agency (ESA) © CNES 2018, distributed by Airbus DS). Satellite image-derived DEMs were field-verified using traditional mapping techniques, which showed a very good agreement between field and remote sensing measures. The high resolution of these tri-stereo images allowed to automatically generate accurate morphometric datasets not only regarding the planimetric parameters of the dolines (diameters, contours, orientation of long axes), but also for what concerns their depth and altimetric profiles. Our study demonstrates the potential of using these types of images on rugged morphologies and for the measurement of steep depressions, where traditional remote sensing techniques may be hindered by shadow zones and blind portions. Tri-stereo images might also be suitable for the measurement of deep and steep depressions (skylights and collapses) on Martian and Lunar lava flows, suitable targets for future planetary cave exploration.

scholarly journals CLOUD REMOVAL FROM SENTINEL-2 IMAGE TIME SERIES THROUGH SPARSE RECONSTRUCTION FROM RANDOM SAMPLES

2016 ◽  
Vol XLI-B3 ◽  
pp. 469-473
Author(s):  
D. Cerra ◽  
J. Bieniarz ◽  
R. Müller ◽  
P. Reinartz
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Reconstruction Error ◽  
Sparse Reconstruction ◽  
Shadow Mask ◽  
Spectral Evolution ◽  
Random Samples ◽  
Restoration Algorithm ◽  
Cloud Removal ◽  
Sentinel 2

In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satellite image time series based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.

scholarly journals Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3524
Author(s):  
Feng Gao ◽  
Martha C. Anderson ◽  
W. Dean Hively
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Cover Crops ◽  
Cover Crop ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Agricultural Landscapes ◽  
Absolute Difference ◽  
Sentinel 2

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

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