Search Results for author: Sébastien Lefèvre

Found 35 papers, 8 papers with code

GeoGraph: Graph-based multi-view object detection with geometric cues end-to-end

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.

object-detection Object Detection

Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder

no code implementations13 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.

Anomaly Detection

Multimodal Object Detection in Remote Sensing

no code implementations13 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.

Object object-detection +1

A Deep Active Contour Model for Delineating Glacier Calving Fronts

no code implementations7 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.

Contour Detection Edge Detection +2

DC3DCD: unsupervised learning for multiclass 3D point cloud change detection

1 code implementation9 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.

Change Detection Image Classification

Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection

1 code implementation5 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.

Change Detection Contrastive Learning +2

Change detection needs change information: improving deep 3D point cloud change detection

1 code implementation25 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.

Change Detection

Learning Digital Terrain Models from Point Clouds: ALS2DTM Dataset and Rasterization-based GAN

no code implementations8 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.

TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation

1 code implementation4 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.

Crop Classification Time Series +2

GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end

no code implementations23 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.

object-detection Object Detection +1

Very high resolution Airborne PolSAR Image Classification using Convolutional Neural Networks

no code implementations31 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.

Classification General Classification +3

Vehicle detection and counting from VHR satellite images: efforts and open issues

no code implementations22 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.

Segmentation

Early Classification for Agricultural Monitoring from Satellite Time Series

no code implementations27 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.

Classification Early Classification +3

BreizhCrops: A Time Series Dataset for Crop Type Mapping

2 code implementations28 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.

Crop Type Mapping Time Series +2

Deep Learning for Classification of Hyperspectral Data: A Comparative Review

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)

General Classification Hyperspectral Image Classification

End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping

2 code implementations30 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.

Classification Crop Classification +6

Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study

no code implementations18 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.

Attribute General Classification +2

Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples

no code implementations7 Jun 2018 Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre

This work addresses the scarcity of annotated hyperspectral data required to train deep neural networks.

Data Augmentation

Recent Developments from Attribute Profiles for Remote Sensing Image Classification

no code implementations27 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.

Attribute Classification +3

Buried object detection from B-scan ground penetrating radar data using Faster-RCNN

no code implementations22 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.

GPR object-detection +1

Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks

1 code implementation23 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.

Towards seamless multi-view scene analysis from satellite to street-level

no code implementations23 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.

Change Detection Earth Observation +3

Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)

no code implementations20 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.

On the usability of deep networks for object-based image analysis

no code implementations22 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.

Earth Observation General Classification +4

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

no code implementations22 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.

Earth Observation Scene Labeling +1

How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?

no code implementations22 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.

Classification General Classification +2

A Subpath Kernel for Learning Hierarchical Image Representations

no code implementations6 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.

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