133 research outputs found
Estimating optical vegetation indices with Sentinel-1 SAR data and AutoML
Current optical vegetation indices (VIs) for monitoring forest ecosystems are
widely used in various applications. However, continuous monitoring based on
optical satellite data can be hampered by atmospheric effects such as clouds.
On the contrary, synthetic aperture radar (SAR) data can offer insightful and
systematic forest monitoring with complete time series due to signal
penetration through clouds and day and night acquisitions. The goal of this
work is to overcome the issues affecting optical data with SAR data and serve
as a substitute for estimating optical VIs for forests using machine learning.
Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated using
multitemporal Sentinel-1 SAR and ancillary data. This was enabled by creating a
paired multi-temporal and multi-modal dataset in Google Earth Engine (GEE),
including temporally and spatially aligned Sentinel-1, Sentinel-2, digital
elevation model (DEM), weather and land cover datasets (MMT-GEE). The use of
ancillary features generated from DEM and weather data improved the results.
The open-source Automatic Machine Learning (AutoML) approach, auto-sklearn,
outperformed Random Forest Regression for three out of four VIs, while a 1-hour
optimization length was enough to achieve sufficient results with an R2 of
69-84% low errors (0.05-0.32 of MAE depending on VI). Great agreement was also
found for selected case studies in the time series analysis and in the spatial
comparison between the original and estimated SAR-based VIs. In general,
compared to VIs from currently freely available optical satellite data and
available global VI products, a better temporal resolution (up to 240
measurements/year) and a better spatial resolution (20 m) were achieved using
estimated SAR-based VIs. A great advantage of the SAR-based VI is the ability
to detect abrupt forest changes with a sub-weekly temporal accuracy.Comment: Full research article. 30 pages, 13 figures, 8 table
Quantum Machine Learning for Remote Sensing: Exploring potential and challenges
The industry of quantum technologies is rapidly expanding, offering promising
opportunities for various scientific domains. Among these emerging
technologies, Quantum Machine Learning (QML) has attracted considerable
attention due to its potential to revolutionize data processing and analysis.
In this paper, we investigate the application of QML in the field of remote
sensing. It is believed that QML can provide valuable insights for analysis of
data from space. We delve into the common beliefs surrounding the quantum
advantage in QML for remote sensing and highlight the open challenges that need
to be addressed. To shed light on the challenges, we conduct a study focused on
the problem of kernel value concentration, a phenomenon that adversely affects
the runtime of quantum computers. Our findings indicate that while this issue
negatively impacts quantum computer performance, it does not entirely negate
the potential quantum advantage in QML for remote sensing.Comment: 2 pages, 2 figures. Presented at the Big Data from Space 2023
conferenc
Apprendre Ă comprendre les images d'observation de la Terre avec des annotations pauvres et non fiables
International audienceIn this paper we discuss the issues of using inexact and inaccurate ground truth in the context of supervised learning. To leverage large amounts of Earth observation data for training algorithms, one often has to use ground truth which was not been carefully assessed. We address both the problems of training and evaluation. We first propose a weakly supervised approach for training change classifiers which is able to detect pixel-level changes in aerial images. We then propose a data poisoning approach to get a reliable estimate of the accuracy that can be expected from a classifier, even when the only ground-truth available does not match the reality. Both are assessed on practical land use and land cover applications
Regards sur un demi-siècle
Le premier numéro du Bulletin des bibliothèques de France est paru en 1956.
Pour marquer ce cinquantenaire, un numéro spécial a été réalisé, comprenant 14 articles de fond sur l\u27évolution des bibliothèques et du métier de bibliothécaire ces cinquante dernières années.
Ce numéro reprend l\u27ancien format de la revue ainsi que la couverture rouge qu\u27il a gardée jusqu\u27en 1979
Learning Speckle Suppression in Sar Images Without Ground Truth: Application to Sentinel-1 Time-Series
International audienceThis paper proposes a method of denoising SAR images, using a deep learning method, which takes advantage of the abundance of data to learn on large stacks of images of the same scene. The approach is based on the use of convolu-tional networks, used as auto-encoders. Learning is led on a large pile of images acquired on the same area, and assumes that the images of this stack differ only by the speckle noise. Several pairs of images are chosen randomly in the stack, and the network tries to predict the slave image from the master image. In this prediction, the network can not predict the noise because of its random nature. Also the application of this network to a new image fulfills the speckle filtering function. Results are given on Sentinel 1 images. They show that this approach is qualitatively competitive with literature
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Hyperspectral unmixing remains one of the most challenging tasks in the
analysis of such data. Deep learning has been blooming in the field and proved
to outperform other classic unmixing techniques, and can be effectively
deployed onboard Earth observation satellites equipped with hyperspectral
imagers. In this letter, we follow this research pathway and propose a
multi-branch convolutional neural network that benefits from fusing spectral,
spatial, and spectral-spatial features in the unmixing process. The results of
our experiments, backed up with the ablation study, revealed that our
techniques outperform others from the literature and lead to higher-quality
fractional abundance estimation. Also, we investigated the influence of
reducing the training sets on the capabilities of all algorithms and their
robustness against noise, as capturing large and representative ground-truth
sets is time-consuming and costly in practice, especially in emerging Earth
observation scenarios.Comment: 14 pages (including supplementary material), published in IEEE
Geoscience and Remote Sensing Letter
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