activity
20162024
most citedA Multibranch Convolutional Neural Network for Hyperspectral Unmixing

20 citations · 67 across the 18 of their papers we have counts for

collaborators

9 papers

cs.CV20232 cited

The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation

Christel Chappuis, Eliot Walt, Vincent Mendez +3

Remote sensing visual question answering (RSVQA) opens new opportunities for the use of overhead imagery by the general public, by enabling human-machine interaction with natural l…

cs.LG20233 cited

A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification

Amer Delilbasic, Bertrand Le Saux, Morris Riedel +2

In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in q…

cs.CV2023

Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines

Artur Miroszewski, Jakub Mielczarek, Grzegorz Czelusta +4

Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with q…

cs.CV202220 cited

A Multibranch Convolutional Neural Network for Hyperspectral Unmixing

Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé +2

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 u…

cs.CV202213 cited

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

Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé +2

Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire m…

eess.IV202119 cited

Weakly Supervised Change Detection Using Guided Anisotropic Difusion

Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch +1

Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms. While these dataset…