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07/14/2023
Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
Support vector machines (SVMs) are a well-established classifier effecti...
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06/26/2023
Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
The optimization of Kernel-Target Alignment (TA) has been recently propo...
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06/16/2023
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Cloud detection is a pivotal satellite image pre-processing step that ca...
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05/15/2023
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and oce...
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03/21/2023
A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification
In recent years, the development of quantum annealers has enabled experi...
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02/16/2023
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
Support vector machines (SVMs) are a well-established classifier effecti...
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10/24/2022
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Cloud detection is a pivotal satellite image pre-processing step that ca...
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08/03/2022
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Hyperspectral unmixing remains one of the most challenging tasks in the ...
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08/03/2022
Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
Maintaining farm sustainability through optimizing the agricultural mana...
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07/01/2022
Rapid training of quantum recurrent neural network
Time series prediction is the crucial task for many human activities e.g...
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04/05/2022
Self-supervised learning – A way to minimize time and effort for precision agriculture?
Machine learning, satellites or local sensors are key factors for a sust...
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03/01/2022
Beyond Ansätze: Learning Quantum Circuits as Unitary Operators
This paper explores the advantages of optimizing quantum circuits on N w...
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01/04/2022
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing
We propose in this article to build up a collaboration between a deep ne...
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01/04/2022
Weakly-supervised continual learning for class-incremental segmentation
Transfer learning is a powerful way to adapt existing deep learning mode...
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12/31/2021
Weakly Supervised Change Detection Using Guided Anisotropic Difusion
Large scale datasets created from crowdsourced labels or openly availabl...
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09/24/2021
How to find a good image-text embedding for remote sensing visual question answering?
Visual question answering (VQA) has recently been introduced to remote s...
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09/20/2021
On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
This article aims to investigate how circuit-based hybrid Quantum Convol...
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07/30/2021
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport
Good quality reconstruction and comprehension of a scene rely on 3D esti...
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10/15/2020
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
The development of semi-supervised learning techniques is essential to e...
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09/23/2020
Interactive Learning for Semantic Segmentation in Earth Observation
Dense pixel-wise classification maps output by deep neural networks are ...
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03/31/2020
DISIR: Deep Image Segmentation with Interactive Refinement
This paper presents an interactive approach for multi-class segmentation...
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11/18/2019
Multi-Task Learning of Height and Semantics from Aerial Images
Aerial or satellite imagery is a great source for land surface analysis,...
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11/04/2019
Technical Report: Co-learning of geometry and semantics for online 3D mapping
This paper is a technical report about our submission for the ECCV 2018 ...
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09/04/2019
Distance transform regression for spatially-aware deep semantic segmentation
Understanding visual scenes relies more and more on dense pixel-wise cla...
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04/17/2019
Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection
Large scale datasets created from user labels or openly available data h...
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10/19/2018
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
The Copernicus Sentinel-2 program now provides multispectral images at a...
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10/19/2018
Fully Convolutional Siamese Networks for Change Detection
This paper presents three fully convolutional neural network architectur...
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10/19/2018
High Resolution Semantic Change Detection
Change detection is one of the main problems in remote sensing, and is e...
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09/05/2018
Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?
Depth estimation is of critical interest for scene understanding and acc...
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06/07/2018
Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples
This work addresses the scarcity of annotated hyperspectral data require...
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11/23/2017
Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks
In this work, we investigate various methods to deal with semantic label...
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05/17/2017
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
In this work, we investigate the use of OpenStreetMap data for semantic ...
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01/20/2017
Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)
In this work, we present a novel module to perform fusion of heterogeneo...
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09/22/2016
How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?
In this paper, we investigate the impact of segmentation algorithms as a...
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09/22/2016
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
This work investigates the use of deep fully convolutional neural networ...
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09/22/2016