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

AIS-based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance

International audienceThis paper studies the performances of different ship detectors based on adaptive threshold algorithms. The detec- tion algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (Automatic Identification System) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels' length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations

Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series

Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multi- and hyperspectral imagery which captures more detailed spectral information concerning the scanned area, hence allows us to benefit from subtle spectral features during the analysis process in agricultural applications. We introduce an approach for extracting 2.5 m cultivated land maps from 10 m Sentinel-2 multispectral image series which benefits from a compact graph convolutional neural network. The experiments indicate that our models not only outperform classical and deep machine learning techniques through delivering higher-quality segmentation maps, but also dramatically reduce the memory footprint when compared to U-Nets (almost 8k trainable parameters of our models, with up to 31M parameters of U-Nets). Such memory frugality is pivotal in the missions which allow us to uplink a model to the AI-powered satellite once it is in orbit, as sending large nets is impossible due to the time constraints.Comment: 7 pages (including supplementary material), published in IEEE Geoscience and Remote Sensing Letter

Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI

Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases. Since both fields advance independently, there is a notable gap in integrating red teaming strategies into RS. This paper introduces a methodology for examining ML models operating on hyperspectral images within the HYPERVIEW challenge, focusing on soil parameters' estimation. We use post-hoc explanation methods from the Explainable AI (XAI) domain to critically assess the best performing model that won the HYPERVIEW challenge and served as an inspiration for the model deployed on board the INTUITION-1 hyperspectral mission. Our approach effectively red teams the model by pinpointing and validating key shortcomings, constructing a model that achieves comparable performance using just 1% of the input features and a mere up to 5% performance loss. Additionally, we propose a novel way of visualizing explanations that integrate domain-specific information about hyperspectral bands (wavelengths) and data transformations to better suit interpreting models for hyperspectral image analysis.Comment: 14 pages, 9 figures, ICLR 2024 Machine Learning for Remote Sensing (ML4RS) Worksho

Fast model inference and training on-board of satellites

Artificial intelligence onboard satellites has the potential to reduce data transmission requirements, enable real-time decision-making and collaboration within constellations. This study deploys a lightweight foundational model called RaVAEn on D-Orbit’s ION SCV004 satellite. RaVAEn is a variational auto-encoder (VAE) that generates compressed latent vectors from small image tiles, enabling several downstream tasks. In this work we demonstrate the reliable use of RaVAEn onboard a satellite, achieving an encoding time of 0.110s for tiles of a 4.8x4.8 km 2 area. In addition, we showcase fast few-shot training onboard a satellite using the latent representation of data. We compare the deployment of the model on the on-board CPU and on the available Myriad vision processing unit (VPU) accelerator. To our knowledge, this work shows for the first time the deployment of a multitask model onboard a CubeSat and the onboard training of a machine learning model

The AutoICE Challenge

Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of the timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The AutoICE Challenge investigates the possibility of creating deep learning models capable of mapping multiple sea ice parameters automatically from spaceborne synthetic aperture radar (SAR) imagery and assesses the current state of the automatic-sea-ice-mapping scientific field. This was achieved by providing the tools and encouraging participants to adopt the paradigm of retrieving multiple sea ice parameters rather than the current focus on single sea ice parameters, such as concentration. The paper documents the efforts and analyses, compares, and discusses the performance of the top-five participants’ submissions. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development, and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and it was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple sea ice parameters with convolutional neural networks and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results

Apport de l'Imagerie SAR Satellitaire en Bandes L et C pour la Caractérisation du Couvert Neigeux.

This thesis concerns snow remote sensing using spaceborne SAR imagery at L- and CBands.An electromagnetic (EM) backscattering model is developed to calculate radar backscatter from snow cover. This model takes into consideration both the vertical snowpack structure and the metamorphosis state of each snow layer. It is validated using in situsnow profiles and SAR data simultaneously acquired by the ASAR/ENVISAT sensor in 2004.The main contribution of this study consists in the combination of dual-polarization SAR data with the meteorological Crocus model developed by Météo-France. To characterize the variability of alpine snowpack, Crocus snow profiles are spatially reorganized by minimizing the difference between simulated and measured C-Band SAR data. Snowcharacteristics maps have been created at SAR resolution level for the French massifs "Grandes Rousses" and "Oisans". The potential of polarimetric L-Band SAR data for snow characterization is investigated in rural areas. A classification method based on Support Vector Machine techniques is developed and evaluated with SAR data acquired by the PALSAR/ALOS sensor.Cette thèse traite de l'apport de l'imagerie SAR satellitaire en bandes L et C pour lacaractérisation du couvert neigeux. Un modèle électromagnétique (EM) permettant de simuler la rétrodiffusion de l'ondesur un couvert neigeux a été développé. Ce modèle prend en considération la structure verticale du manteau neigeux ainsi que l'état de métamorphose des différentes couches. Il est validé à l'aide de profils stratigraphiques mesurés et des données SAR acquisesparallèlement par le capteur ASAR/ENVISAT en 2004.L'originalité principale de cette étude consiste en l'association des données SAR à polarisation double avec le modèle météorologique Crocus développé par Météo-France.Dans le but de caractériser la variabilité spatiale des couverts neigeux alpins, les profils stratigraphiques Crocus sont réorganisés spatialement par le biais d'une optimisation de la réponse EM en bande C. Des cartographies du couvert neigeux sont réalisées avec une résolution métrique pour les massifs alpins des Grandes Rousses et de l'Oisans.Finalement, le potentiel des données polarimétriques en bande L pour la caractérisation de la neige est étudié sur des zones rurales. Une méthode de cartographie basée sur les Machines à Vecteurs Supports est réalisée puis testée avec des données acquises par lecapteur PALSAR/ALOS

Toward an operational method for refined characterization using dual-Polarization C-Band SAR data.

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Contribution de l'imagerie SAR Satellitaire en Bande C pour la Simulation du Couvert Neigeux : vers une amélioration du modèle météorologique Crocus.

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