no code implementations • ECCV 2020 • Ahmed Samy Nassar, Stefano D’Aronco, Sébastien Lefèvre, Jan D. Wegner
In this paper, we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object.
no code implementations • 15 Jan 2024 • Mathilde Letard, Dimitri Lague, Arthur Le Guennec, Sébastien Lefèvre, Baptiste Feldmann, Paul Leroy, Daniel Girardeau-Montaut, Thomas Corpetti
In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow.
no code implementations • 13 Jul 2023 • Abdelbadie Belmouhcine, Jean-Christophe Burnel, Luc Courtrai, Minh-Tan Pham, Sébastien Lefèvre
Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques.
no code implementations • 13 Jul 2023 • Minh-Tan Pham, Hugo Gangloff, Sébastien Lefèvre
This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments.
no code implementations • 7 Jul 2023 • Konrad Heidler, Lichao Mou, Erik Loebel, Mirko Scheinert, Sébastien Lefèvre, Xiao Xiang Zhu
Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps.
1 code implementation • 9 May 2023 • Iris de Gélis, Sébastien Lefèvre, Thomas Corpetti
In this paper, we propose an unsupervised method, called DeepCluster 3D Change Detection (DC3DCD), to detect and categorize multiclass changes at point level.
1 code implementation • 5 May 2023 • Iris de Gélis, Sudipan Saha, Muhammad Shahzad, Thomas Corpetti, Sébastien Lefèvre, Xiao Xiang Zhu
To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning.
1 code implementation • 25 Apr 2023 • Iris de Gélis, Thomas Corpetti, Sébastien Lefèvre
While deep learning has recently proven its effectiveness for this particular task by encoding the information through Siamese networks, we investigate herein the idea of also using change information in the early steps of deep networks.
no code implementations • 8 Jun 2022 • Hoàng-Ân Lê, Florent Guiotte, Minh-Tan Pham, Sébastien Lefèvre, Thomas Corpetti
Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging.
no code implementations • 14 Apr 2022 • Wei-Hsin Tseng, Hoàng-Ân Lê, Alexandre Boulch, Sébastien Lefèvre, Dirk Tiede
It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery.
no code implementations • 14 Apr 2022 • Anne Achieng Osio, Hoàng-Ân Lê, Samson Ayugi, Fred Onyango, Peter Odwe, Sébastien Lefèvre
Deep-learning-based image classification and object detection has been applied successfully to tree monitoring.
1 code implementation • 4 Nov 2021 • Joachim Nyborg, Charlotte Pelletier, Sébastien Lefèvre, Ira Assent
However, when applied to target regions spatially different from the training region, these models perform poorly without any target labels due to the temporal shift of crop phenology between regions.
no code implementations • ICLR Workshop EBM 2021 • Javiera Castillo Navarro, Bertrand Le Saux, Alexandre Boulch, Sébastien Lefèvre
The large amount of data, available thanks to the recent sensors, have made possible the use of deep learning for Earth Observation.
no code implementations • 15 Oct 2020 • Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch, Nicolas Audebert, Sébastien Lefèvre
The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms.
no code implementations • 23 Mar 2020 • Ahmed Samy Nassar, Stefano D'Aronco, Sébastien Lefèvre, Jan D. Wegner
In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object.
no code implementations • 31 Oct 2019 • Minh-Tan Pham, Sébastien Lefèvre
In this work, we exploit convolutional neural networks (CNNs) for the classification of very high resolution (VHR) polarimetric SAR (PolSAR) data.
no code implementations • 22 Oct 2019 • Alice Froidevaux, Andréa Julier, Agustin Lifschitz, Minh-Tan Pham, Romain Dambreville, Sébastien Lefèvre, Pierre Lassalle, Thanh-Long Huynh
Detection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban growth.
no code implementations • 4 Sep 2019 • Nicolas Audebert, Alexandre Boulch, Bertrand Le Saux, Sébastien Lefèvre
This work introduces a new semantic segmentation regularization based on the regression of a distance transform.
no code implementations • 27 Aug 2019 • Marc Rußwurm, Romain Tavenard, Sébastien Lefèvre, Marco Körner
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring.
2 code implementations • 28 May 2019 • Marc Rußwurm, Charlotte Pelletier, Maximilian Zollner, Sébastien Lefèvre, Marco Körner
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series.
1 code implementation • IEEE Geoscience and Remote Sensing Magazine 2019 • Nicolas Audebert, Bertrand Saux, Sébastien Lefèvre
1 This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deep learning techniques to their own dataset.
Ranked #14 on Hyperspectral Image Classification on Pavia University (Overall Accuracy metric)
2 code implementations • 30 Jan 2019 • Marc Rußwurm, Nicolas Courty, Rémi Emonet, Sébastien Lefèvre, Devis Tuia, Romain Tavenard
In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
no code implementations • 18 Jun 2018 • Minh-Tan Pham, Erchan Aptoula, Sébastien Lefèvre
The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures.
no code implementations • 7 Jun 2018 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
This work addresses the scarcity of annotated hyperspectral data required to train deep neural networks.
no code implementations • 27 Mar 2018 • Minh-Tan Pham, Sébastien Lefèvre, Erchan Aptoula, Lorenzo Bruzzone
Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially for classification task.
no code implementations • 22 Mar 2018 • Minh-Tan Pham, Sébastien Lefèvre
In this paper, we adapt the Faster-RCNN framework for the detection of underground buried objects (i. e. hyperbola reflections) in B-scan ground penetrating radar (GPR) images.
1 code implementation • 23 Nov 2017 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data.
no code implementations • 23 May 2017 • Sébastien Lefèvre, Devis Tuia, Jan Dirk Wegner, Timothée Produit, Ahmed Samy Nassar
In this paper, we discuss and review how combined multi-view imagery from satellite to street-level can benefit scene analysis.
no code implementations • 17 May 2017 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images.
no code implementations • 20 Jan 2017 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture.
no code implementations • 22 Sep 2016 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images.
no code implementations • 22 Sep 2016 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this work, we show how to use such deep networks to detect, segment and classify different varieties of wheeled vehicles in aerial images from the ISPRS Potsdam dataset.
no code implementations • 22 Sep 2016 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework.
no code implementations • 15 Jun 2016 • Yanwei Cui, Laetitia Chapel, Sébastien Lefèvre
Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy.
Classification Of Hyperspectral Images General Classification +1
no code implementations • 6 Apr 2016 • Yanwei Cui, Laetitia Chapel, Sébastien Lefèvre
This is particularly true in remote sensing, where such representations allow for revealing different objects of interest at various scales through a tree structure.