Jocelyn Chanussot

research

∙ 09/04/2023

Neural network-based emulation of interstellar medium models

The interpretation of observations of atomic and molecular tracers in th...

0 Pierre Palud, et al. ∙

research

∙ 08/18/2023

Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

Spectral pixels are often a mixture of the pure spectra of the materials...

0 Behnood Rasti, et al. ∙

research

∙ 08/09/2023

SUnAA: Sparse Unmixing using Archetypal Analysis

This paper introduces a new sparse unmixing technique using archetypal a...

0 Behnood Rasti, et al. ∙

research

∙ 10/21/2022

Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection

In defense-related remote sensing applications, such as vehicle detectio...

0 Jules BOURCIER, et al. ∙

research

∙ 10/13/2022

Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery

The application of deep neural networks to remote sensing imagery is oft...

0 Jules BOURCIER, et al. ∙

research

∙ 09/22/2022

Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing

In this paper, we introduce a new algorithm based on archetypal analysis...

9 Alexandre Zouaoui, et al. ∙

research

∙ 06/24/2022

Self Supervised Learning for Few Shot Hyperspectral Image Classification

Deep learning has proven to be a very effective approach for Hyperspectr...

0 Nassim Ait Ali Braham, et al. ∙

research

∙ 04/18/2022

Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives

In recent years, supervised learning has been widely used in various tas...

4 Jun Yue, et al. ∙

research

∙ 12/04/2021

A Triple-Double Convolutional Neural Network for Panchromatic Sharpening

Pansharpening refers to the fusion of a panchromatic image with a high s...

7 Tian-Jing Zhang, et al. ∙

research

∙ 11/26/2021

Geometric Multimodal Deep Learning with Multi-Scaled Graph Wavelet Convolutional Network

Multimodal data provide complementary information of a natural phenomeno...

0 Maysam Behmanesh, et al. ∙

research

∙ 11/18/2021

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration

Hyperspectral imaging offers new perspectives for diverse applications, ...

3 Théo Bodrito, et al. ∙

research

∙ 08/18/2021

Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images

Object detection is a challenging task in remote sensing because objects...

11 Pourya Shamsolmoali, et al. ∙

research

∙ 07/27/2021

Unsupervised Outlier Detection using Memory and Contrastive Learning

Outlier detection is one of the most important processes taken to create...

12 Ning Huyan, et al. ∙

research

∙ 06/02/2021

Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery

Over the last few years, there has been substantial progress in object d...

11 Pourya Shamsolmoali, et al. ∙

research

∙ 05/12/2021

Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization

Multimodal manifold modeling methods extend the spectral geometry-aware ...

0 Maysam Behmanesh, et al. ∙

research

∙ 05/10/2021

An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery

Semantic segmentation is an essential part of deep learning. In recent y...

23 Xuan Yang, et al. ∙

research

∙ 03/30/2021

Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing

Tensor-based methods have been widely studied to attack inverse problems...

0 Lianru Gao, et al. ∙

research

∙ 08/06/2020

Graph Convolutional Networks for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have been attracting increasing att...

14 Danfeng Hong, et al. ∙

research

∙ 07/28/2020

Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution

Due to the limitations of hyperspectral imaging systems, hyperspectral i...

0 Ke Zheng, et al. ∙

research

∙ 07/28/2020

Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning

Extensive attention has been widely paid to enhance the spatial resoluti...

0 Lianru Gao, et al. ∙

research

∙ 07/17/2020

Spatial-Spectral Manifold Embedding of Hyperspectral Data

In recent years, hyperspectral imaging, also known as imaging spectrosco...

0 Danfeng Hong, et al. ∙

research

∙ 07/10/2020

Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution

The recent advancement of deep learning techniques has made great progre...

0 Jing Yao, et al. ∙

research

∙ 06/24/2020

X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data

This paper addresses the problem of semi-supervised transfer learning wi...

0 Danfeng Hong, et al. ∙

research

∙ 05/29/2020

Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks

Hyperspectral images are of crucial importance in order to better unders...

32 Jin-Fan Hu, et al. ∙

research

∙ 04/02/2020

Graph-based fusion for change detection in multi-spectral images

In this paper we address the problem of change detection in multi-spectr...

0 David Alejandro Jimenez Sierra, et al. ∙

research

∙ 03/05/2020

Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep

Hyperspectral images provide detailed spectral information through hundr...

7 Behnood Rasti, et al. ∙

research

∙ 12/18/2019

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Up to the present, an enormous number of advanced techniques have been d...

22 Danfeng Hong, et al. ∙

research

∙ 12/18/2019

Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data

Due to the ever-growing diversity of the data source, multi-modality fea...

0 Danfeng Hong, et al. ∙

research

∙ 06/04/2019

An Introduction to Deep Morphological Networks

The recent impressive results of deep learning-based methods on computer...

0 Keiller Nogueira, et al. ∙

research

∙ 05/27/2019

Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

Geospatial object detection of remote sensing imagery has been attractin...

0 Xin Wu, et al. ∙

research

∙ 01/23/2019

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

With the rapid development of spaceborne imaging techniques, object dete...

24 Xin Wu, et al. ∙

research

∙ 01/09/2019

Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification

In this paper, we aim at tackling a general but interesting cross-modali...

0 Danfeng Hong, et al. ∙

research

∙ 12/30/2018

CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

With a large amount of open satellite multispectral imagery (e.g., Senti...

16 Danfeng Hong, et al. ∙

research

∙ 10/29/2018

An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

Hyperspectral imagery collected from airborne or satellite sources inevi...

0 Danfeng Hong, et al. ∙

research

∙ 04/11/2018

Dynamic Multi-Scale Semantic Segmentation based on Dilated Convolutional Networks

Semantic segmentation requires methods capable of learning high-level fe...

2 Keiller Nogueira, et al. ∙

research

∙ 11/13/2017

Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation

Image segmentation is considered to be one of the critical tasks in hype...

0 Fahim Irfan Alam, et al. ∙

research

∙ 02/03/2016

A Framework for Fast Image Deconvolution with Incomplete Observations

In image deconvolution problems, the diagonalization of the underlying o...

0 Miguel Simões, et al. ∙

research

∙ 10/14/2015

Dynamical spectral unmixing of multitemporal hyperspectral images

In this paper, we consider the problem of unmixing a time series of hype...

0 Simon Henrot, et al. ∙

research

∙ 04/17/2015

Hyperspectral pansharpening: a review

Pansharpening aims at fusing a panchromatic image with a multispectral o...

0 Laetitia Loncan, et al. ∙

research

∙ 11/14/2014

A convex formulation for hyperspectral image superresolution via subspace-based regularization

Hyperspectral remote sensing images (HSIs) usually have high spectral re...

0 Miguel Simões, et al. ∙

research

∙ 03/31/2014

Hyperspectral image superresolution: An edge-preserving convex formulation

Hyperspectral remote sensing images (HSIs) are characterized by having a...

0 Miguel Simões, et al. ∙

research

∙ 02/28/2012

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their ...

0 Jose M. Bioucas-Dias, et al. ∙

Jocelyn Chanussot

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