Search Results for author: Jocelyn Chanussot

Found 68 papers, 27 papers with code

Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events

1 code implementation14 May 2024 Xin Wu, Zhanchao Huang, Li Wang, Jocelyn Chanussot, Jiaojiao Tian

To this end, we first construct two multimodal dense and occlusion vehicle detection datasets for large-scale events, utilizing RGB and height map modalities.

4k object-detection +1

SpectralMamba: Efficient Mamba for Hyperspectral Image Classification

1 code implementation12 Apr 2024 Jing Yao, Danfeng Hong, Chenyu Li, Jocelyn Chanussot

Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences.

Classification Hyperspectral Image Classification +1

Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection

no code implementations23 Feb 2024 Chenyu Li, Bing Zhang, Danfeng Hong, Jing Yao, Jocelyn Chanussot

These factors also limit the performance of the well-known low-rank representation (LRR) models in terms of robustness on the separation of background and target features and the reliance on manual parameter selection.

Anomaly Detection

Fast Semi-supervised Unmixing using Non-convex Optimization

1 code implementation23 Jan 2024 Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot

Our experimental results validate that enforcing the convexity constraint outperforms the sparsity prior for the endmember library.

SpectralGPT: Spectral Remote Sensing Foundation Model

no code implementations13 Nov 2023 Danfeng Hong, Bing Zhang, Xuyang Li, YuXuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, Hao Li, Pedram Ghamisi, Xiuping Jia, Antonio Plaza, Paolo Gamba, Jon Atli Benediktsson, Jocelyn Chanussot

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.

Change Detection Representation Learning +3

Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation

no code implementations28 Oct 2023 Pourya Shamsolmoali, Jocelyn Chanussot, Huiyu Zhou, Yue Lu

To address the aforementioned challenges, we propose Attention-based Feature Distillation (AFD), a new KD approach that distills both local and global information from the teacher detector.

Knowledge Distillation Object +2

Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks

no code implementations26 Sep 2023 Danfeng Hong, Bing Zhang, Hao Li, YuXuan Li, Jing Yao, Chenyu Li, Martin Werner, Jocelyn Chanussot, Alexander Zipf, Xiao Xiang Zhu

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e. g., single cities or regions).

Domain Adaptation Segmentation +1

SUnAA: Sparse Unmixing using Archetypal Analysis

1 code implementation9 Aug 2023 Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot

Unlike most conventional sparse unmixing methods, here the minimization problem is non-convex.

UIU-Net: U-Net in U-Net for Infrared Small Object Detection

1 code implementation2 Dec 2022 Xin Wu, Danfeng Hong, Jocelyn Chanussot

RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information.

Object object-detection +2

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

no code implementations21 Oct 2022 Jules BOURCIER, Thomas Floquet, Gohar Dashyan, Tugdual Ceillier, Karteek Alahari, Jocelyn Chanussot

In defense-related remote sensing applications, such as vehicle detection on satellite imagery, supervised learning requires a huge number of labeled examples to reach operational performances.

object-detection Object Detection +2

Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing

1 code implementation22 Sep 2022 Alexandre Zouaoui, Gedeon Muhawenayo, Behnood Rasti, Jocelyn Chanussot, Julien Mairal

In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers.

Hyperspectral Unmixing Model Selection

A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation

no code implementations18 May 2022 Na Liu, Wei Li, Yinjian Wang, Rao Tao, Qian Du, Jocelyn Chanussot

The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy.

Deblurring Denoising +2

Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review

no code implementations13 May 2022 Minghua Wang, Danfeng Hong, Zhu Han, Jiaxin Li, Jing Yao, Lianru Gao, Bing Zhang, Jocelyn Chanussot

Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite.

Anomaly Detection Super-Resolution +1

Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images

no code implementations12 May 2022 Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Jocelyn Chanussot, Jie Yang

In IPSSD, single-shot detector is adopted combined with an image pyramid network to extract semantically strong features for generating candidate regions.

Object object-detection +1

Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion

1 code implementation7 May 2022 Danfeng Hong, Jing Yao, Deyu Meng, Naoto Yokoya, Jocelyn Chanussot

Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images.

Hyperspectral Image Super-Resolution Image Super-Resolution +1

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review

no code implementations3 May 2022 Jiaxin Li, Danfeng Hong, Lianru Gao, Jing Yao, Ke Zheng, Bing Zhang, Jocelyn Chanussot

With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way.

Earth Observation

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

no code implementations18 Apr 2022 Jun Yue, Leyuan Fang, Pedram Ghamisi, Weiying Xie, Jun Li, Jocelyn Chanussot, Antonio J Plaza

Therefore, remote sensing image understanding often faces the problems of incomplete, inexact, and inaccurate supervised information, which will affect the breadth and depth of remote sensing applications.

Change Detection Image Classification +4

Multimodal Fusion Transformer for Remote Sensing Image Classification

2 code implementations31 Mar 2022 Swalpa Kumar Roy, Ankur Deria, Danfeng Hong, Behnood Rasti, Antonio Plaza, Jocelyn Chanussot

Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs).

Classification Image Classification +2

A Triple-Double Convolutional Neural Network for Panchromatic Sharpening

no code implementations4 Dec 2021 Tian-Jing Zhang, Liang-Jian Deng, Ting-Zhu Huang, Jocelyn Chanussot, Gemine Vivone

Pansharpening refers to the fusion of a panchromatic image with a high spatial resolution and a multispectral image with a low spatial resolution, aiming to obtain a high spatial resolution multispectral image.


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

no code implementations26 Nov 2021 Maysam Behmanesh, Peyman Adibi, Mohammad Saeed Ehsani, Jocelyn Chanussot

Capturing the intra-modality and cross-modality information of multimodal data is the essential capability of multimodal learning methods.

Multimodal Deep Learning Node Classification

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

2 code implementations NeurIPS 2021 Théo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot, Julien Mairal

Hyperspectral imaging offers new perspectives for diverse applications, ranging from the monitoring of the environment using airborne or satellite remote sensing, precision farming, food safety, planetary exploration, or astrophysics.

Denoising Hyperspectral Image Denoising +1

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

no code implementations18 Aug 2021 Pourya Shamsolmoali, Jocelyn Chanussot, Masoumeh Zareapoor, Huiyu Zhou, Jie Yang

Second, most of the standard methods used hand-crafted features, and do not work well on the detection of objects parts of which are missing.

Object object-detection +1

Unsupervised Outlier Detection using Memory and Contrastive Learning

no code implementations27 Jul 2021 Ning Huyan, Dou Quan, Xiangrong Zhang, Xuefeng Liang, Jocelyn Chanussot, Licheng Jiao

Instead, we think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers.

Contrastive Learning Outlier Detection

SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers

2 code implementations7 Jul 2021 Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot

Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.

Classification Hyperspectral Image Classification

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

no code implementations2 Jun 2021 Pourya Shamsolmoali, Masoumeh Zareapoor, Jocelyn Chanussot, Huiyu Zhou, Jie Yang

The proposed model adopts single-shot detector in parallel with a lightweight image pyramid module to extract representative features and generate regions of interest in an optimization approach.

Object object-detection +1

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

1 code implementation21 May 2021 Danfeng Hong, Lianru Gao, Jing Yao, Naoto Yokoya, Jocelyn Chanussot, Uta Heiden, Bing Zhang

Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneously generalize various spectral variabilities and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various spectral variabilities.

Hyperspectral Unmixing

Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model

1 code implementation21 May 2021 Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, Xiao Xiang Zhu

Moreover, to better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i. e., Houston2013 -- hyperspectral and multispectral data, Berlin -- hyperspectral and synthetic aperture radar (SAR) data, Augsburg -- hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification.

Land Cover Classification

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

no code implementations12 May 2021 Maysam Behmanesh, Peyman Adibi, Jocelyn Chanussot, Sayyed Mohammad Saeed Ehsani

The second method is a manifold regularized multimodal classification based on pointwise correspondences (M$^2$CPC) used for the problem of multiclass classification of multimodal heterogeneous, which the correspondences between modalities are determined based on the FMBSD method.

Classification Cross-Modal Retrieval +1

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

no code implementations30 Mar 2021 Lianru Gao, Zhicheng Wang, Lina Zhuang, Haoyang Yu, Bing Zhang, Jocelyn Chanussot

Tensor-based methods have been widely studied to attack inverse problems in hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented as a third-order tensor, which can perfectly retain the spatial information in the image.

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

no code implementations2 Mar 2021 Danfeng Hong, wei he, Naoto Yokoya, Jing Yao, Lianru Gao, Liangpei Zhang, Jocelyn Chanussot, Xiao Xiang Zhu

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS).

Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction

1 code implementation21 Sep 2020 Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Jian Xu, Xiao Xiang Zhu

Conventional nonlinear subspace learning techniques (e. g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination).

Dimensionality Reduction

PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field

no code implementations13 Sep 2020 Haixia Bi, Jing Yao, Zhiqiang Wei, Danfeng Hong, Jocelyn Chanussot

Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications.

Classification Data Augmentation +2

Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR Data

1 code implementation IEEE Geoscience and Remote Sensing Letters 2020 Danfeng Hong, Lianru Gao, Renlong Hang, Bing Zhang, Jocelyn Chanussot

To overcome this limitation, we present a simple but effective multimodal DL baseline by following a deep encoder–decoder network architecture, EndNet for short, for the classification of hyperspectral and light detection and ranging (LiDAR) data.

Classification Decoder

More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification

1 code implementation12 Aug 2020 Danfeng Hong, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, Bing Zhang

In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications.

Classification General Classification +2

Graph Convolutional Networks for Hyperspectral Image Classification

1 code implementation6 Aug 2020 Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot

Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations.

Classification General Classification +1

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

1 code implementation28 Jul 2020 Ke Zheng, Lianru Gao, Wenzhi Liao, Danfeng Hong, Bing Zhang, Ximin Cui, Jocelyn Chanussot

In this work, an unsupervised deep learning-based fusion method - HyCoNet - that can solve the problems in HSI-MSI fusion without the prior PSF and SRF information is proposed.


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

1 code implementation28 Jul 2020 Lianru Gao, Danfeng Hong, Jing Yao, Bing Zhang, Paolo Gamba, Jocelyn Chanussot

However, the ability in the fusion of HS and MS images remains to be improved, particularly in large-scale scenes, due to the limited acquisition of HS images.

Spatial-Spectral Manifold Embedding of Hyperspectral Data

no code implementations17 Jul 2020 Danfeng Hong, Jing Yao, Xin Wu, Jocelyn Chanussot, Xiao Xiang Zhu

In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community.

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

no code implementations29 May 2020 Jin-Fan Hu, Ting-Zhu Huang, Liang-Jian Deng, Tai-Xiang Jiang, Gemine Vivone, Jocelyn Chanussot

In order to alleviate this issue, in this work, we propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution hyperspectral image (HR-HSI).

Hyperspectral Image Super-Resolution Image Super-Resolution

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

no code implementations2 Apr 2020 David Alejandro Jimenez Sierra, Hernán Darío Benítez Restrepo, Hernán Darío Vargas Cardonay, Jocelyn Chanussot

In this paper we address the problem of change detection in multi-spectral images by proposing a data-driven framework of graph-based data fusion.

Change Detection

Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)

1 code implementation5 Mar 2020 Behnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson

The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques.

General Classification Hyperspectral Image Classification

Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review

1 code implementation21 Jan 2020 Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jocelyn Chanussot, Lucas. Drumetz, Jean-Yves Tourneret, Alina Zare, Christian Jutten

The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.

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

no code implementations18 Dec 2019 Danfeng Hong, Jocelyn Chanussot, Naoto Yokoya, Jian Kang, Xiao Xiang Zhu

Due to the ever-growing diversity of the data source, multi-modality feature learning has attracted more and more attention.

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

no code implementations18 Dec 2019 Danfeng Hong, Xin Wu, Pedram Ghamisi, Jocelyn Chanussot, Naoto Yokoya, Xiao Xiang Zhu

In this paper, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs).

Attribute General Classification +1

Online Structured Sparsity-based Moving Object Detection from Satellite Videos

no code implementations29 Nov 2019 Junpeng Zhang, Xiuping Jia, Jiankun Hu, Jocelyn Chanussot

O-LSD reformulates the batch-based low-rank matrix decomposition with the structured sparse penalty to its equivalent frame-wise separable counterpart, which then defines a stochastic optimization problem for online subspace basis estimation.

Moving Object Detection object-detection +1

An Introduction to Deep Morphological Networks

no code implementations4 Jun 2019 Keiller Nogueira, Jocelyn Chanussot, Mauro Dalla Mura, Jefersson A. dos Santos

Results show that the proposed DeepMorphNets is a promising technique that can learn distinct features when compared to the ones learned by current deep learning methods.

Image Classification

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

no code implementations27 May 2019 Xin Wu, Danfeng Hong, Jocelyn Chanussot, Yang Xu, Ran Tao, Yue Wang

To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB).

Object object-detection +1

Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-Net

1 code implementation27 Apr 2019 Ying Qu, Hairong Qi, Chiman Kwan, Naoto Yokoya, Jocelyn Chanussot

With this design, the network allows to extract correlated spectral and spatial information from unregistered images that better preserves the spectral information.

Hyperspectral Image Super-Resolution Image Super-Resolution

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

no code implementations23 Jan 2019 Xin Wu, Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Wei Li, Ran Tao

To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy.

Novel Object Detection object-detection +1

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

no code implementations9 Jan 2019 Danfeng Hong, Naoto Yokoya, Nan Ge, Jocelyn Chanussot, Xiao Xiang Zhu

In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community --- can a limited amount of highly-discrimin-ative (e. g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discriminative (e. g., multispectral) data?

General Classification

CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

no code implementations30 Dec 2018 Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu

To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification.

Classification General Classification +1

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

no code implementations29 Oct 2018 Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu

To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing.

Dictionary Learning Hyperspectral Unmixing

Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks

1 code implementation11 Apr 2018 Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William R. Schwartz, Jefersson A. dos Santos

A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties.

Semantic Segmentation

A Framework for Fast Image Deconvolution with Incomplete Observations

1 code implementation3 Feb 2016 Miguel Simões, Luis B. Almeida, José Bioucas-Dias, Jocelyn Chanussot

In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast.

Demosaicking Image Deconvolution

Hyperspectral pansharpening: a review

no code implementations17 Apr 2015 Laetitia Loncan, Luis B. Almeida, José M. Bioucas-Dias, Xavier Briottet, Jocelyn Chanussot, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio A. Licciardi, Miguel Simões, Jean-Yves Tourneret, Miguel A. Veganzones, Gemine Vivone, Qi Wei, Naoto Yokoya

In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data.


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

no code implementations14 Nov 2014 Miguel Simões, José Bioucas-Dias, Luis B. Almeida, Jocelyn Chanussot

Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution.

Hyperspectral image superresolution: An edge-preserving convex formulation

no code implementations31 Mar 2014 Miguel Simões, José Bioucas-Dias, Luis B. Almeida, Jocelyn Chanussot

Hyperspectral remote sensing images (HSIs) are characterized by having a low spatial resolution and a high spectral resolution, whereas multispectral images (MSIs) are characterized by low spectral and high spatial resolutions.

Cannot find the paper you are looking for? You can Submit a new open access paper.