Jocelyn Chanussot

Jocelyn Chanussot
Grenoble Institute of Technology | Grenoble INP · GIPSA-lab of Images, Signal, Speech and Automatics

About

755
Publications
126,425
Reads
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33,234
Citations
Citations since 2016
364 Research Items
25954 Citations
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201620172018201920202021202201,0002,0003,0004,0005,000
201620172018201920202021202201,0002,0003,0004,0005,000
Additional affiliations
September 2007 - present
Grenoble Institute of Technology
Position
  • Professor (Full)

Publications

Publications (755)
Article
Full-text available
All artificial intelligence models today require preprocessed and cleaned data to work properly. This crucial step depends on the quality of the data analysis being done. The Space Weather community increased its use of AI in the past few years, but a thorough data analysis addressing all the potential issues is not always performed beforehand. Her...
Article
This article addresses the problem of the building an out-of-the-box deep detector, motivated by the need to perform anomaly detection across multiple hyperspectral images (HSIs) without repeated training. To solve this challenging task, we propose a unified detector anomaly detection network (AUD-Net) inspired by few-shot learning. The crucial iss...
Article
Multimodal data provide complementary information of a natural phenomenon by integrating data from various domains with very different statistical properties. Capturing the intramodality and cross-modality information of multimodal data is the essential capability of multimodal learning methods. The geometry-aware data analysis approaches provide t...
Article
Outlier detection is to separate anomalous data from inliers in the dataset. Recently, the most deep learning methods of outlier detection leverage an auxiliary reconstruction task by assuming that outliers are more difficult to recover than normal samples (inliers). However, it is not always true in deep auto-encoder (AE) based models. The auto-en...
Preprint
Full-text available
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural formulation for this task. This method does not require the presence of pure pixels (i.e., pixels containing a single material) but instead represents endmembers as convex...
Article
The band selection (BS) is an essential task in hyperspectral images (HSIs), consisting of huge spectral bands with noises and redundancies. The attention mechanism can be used for BS. However, the existing attention-based BS schemes are failed to capture the cross-dimension interaction between the input spectral and spatial dimensions during compu...
Article
Full-text available
The ionization fraction in neutral interstellar clouds is a key physical parameter controlling multiple physical and chemical processes, and varying by orders of magnitude from the UV irradiated surface of the cloud to its cosmic-ray dominated central regions. Traditional observational tracers of the ionization fraction, which mostly rely on deuter...
Article
Full-text available
Atoms and molecules have long been thought to be versatile tracers of the cold neutral gas in the universe, from high-redshift galaxies to star forming regions and proto-planetary disks, because their internal degrees of freedom bear the signature of the physical conditions where these species reside. However, the promise that molecular emission ha...
Article
Full-text available
Machine learning (ML) is influencing the literature in several research fields, often through state-of-theart approaches. In the past several years, ML has been explored for pansharpening, i.e., an image fusion technique based on the combination of a multispectral (MS) image, which is characterized by its medium/low spatial resolution, and higher-s...
Article
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In this letter, a novel neural architecture search (NAS) method based on reinforcement learning, called RLNAS, is devised to realize the automatic architecture design in the field of hyperspectral unmixing (HU). This method first train the search network in the constructed self-supervised datasets based on hyperspectral images. The block-based sear...
Article
The launch of small satellites, CubeSats among others, is skyrocketing easing the development of Earth Observation missions and the implementation of Space applications. At the same time, Artificial Intelligence and deep neural networks algorithms are enjoying impressive success for their results and the diverse applications they enable. The idea o...
Article
Convolutional Neural Networks (CNNs) have been extensively studied for Hyperspectral Image Classification (HSIC). However, CNNs are critically attributed to a large number of labeled training samples, which outlays high costs in terms of time and resources. Moreover, CNNs are trained on some samples and have been tested on the entire HSI. Perhaps,...
Article
italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Panchromatic ( Pan ) sharpening , or pansharpening , refers to the combination of a multispectral (MS) image and Pan data with a finer spatial resolution. Since the early days of this research topic, the issue of quality assessment has played a ce...
Article
Full-text available
Documenting the inter-annual variability and the long-term trend of the glacier snow line altitude is highly relevant to document the evolution of glacier mass changes. Automatically identifying the snow line on glaciers is challenging; recent developments in machine learning approaches show promise to tackle this issue. This manuscript presents a...
Article
Land cover classification (LCC) is an important application in remote sensing data interpretation and invariably faces big intra-class variance and sample imbalance in remote sensing images. The optical image is obtained by satellites capturing the spectral information of the Earth’s surface, and the synthetic aperture radar (SAR) image is produced...
Article
Full-text available
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity are readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. With the joint utilization of EO data, much resea...
Article
Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress. To further enhance the performance with few labeled samples, we in this paper propose a novel FSL framework for HSIC with a class-covariance metric (CMFSL). Overall, the CMFSL learns globa...
Article
Full-text available
Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors. Therefore, for the application of hyperspectral images, unmixing technology is a key processing technology, such as linear mixing model and its derived algorit...
Article
Polarimetric synthetic aperture radar (PolSAR) data can be acquired at all times and are not impacted by weather conditions. They can efficiently capture geometrical and geographical structures on the ground. However, due to the complexity of the data and the difficulty of data availability, PolSAR image scene classification remains a challenging t...
Article
Full-text available
Owing to the powerful and automatic representation capabilities, deep learning (DL) techniques have made significant breakthroughs and progress in hyperspectral unmixing (HU). Among the DL approaches, autoencoders (AEs) have become a widely-used and promising network architecture. However, these AE-based methods heavily rely on manual design and ma...
Article
In recent years, supervised learning has been widely used in various tasks of optical remote sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation, change detection, and object detection. The methods based on supervised learning need a large amount of high-quality training data, and their performance highly depend...
Article
Recently, low-rank representation (LRR) methods have been widely applied for hyperspectral anomaly detection, due to their potentials in separating the backgrounds and anomalies. However, existing LRR models generally convert 3-D hyperspectral images (HSIs) into 2-D matrices, inevitably leading to the destruction of intrinsic 3-D structure properti...
Article
Convolutional Neural Networks (CNNs) have been extensively utilized for Hyperspectral (HSI) as well as Light Detection and Ranging (LiDAR) data Classification. However, CNNs have not been much explored for joint HSI and LiDAR image classification. Therefore, this article proposes a joint feature learning (HSI and LiDAR) and fusion mechanism using C...
Preprint
Full-text available
The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental d...
Preprint
Full-text available
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. The recent advancement and even revo...
Preprint
Full-text available
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perform the fusion task by means of multifarious pixel-level priors. Yet the intrinsic effects of a large distribution gap between HS-MS data due to differences in the spatia...
Preprint
Full-text available
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. With the joint utilization of EO data, much resear...
Article
Full-text available
Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots in the field of remote sensing. Because HSI’s features of integrating image and spectrum provide a considerable data basis for abnormal object detection, HSI-AD has a huge application potential in HSI analysis. It is difficult to effectively extract a large numbe...
Preprint
In recent years, supervised learning has been widely used in various tasks of optical remote sensing image understanding, including remote sensing image classification, pixel-wise segmentation, change detection, and object detection. The methods based on supervised learning need a large amount of high-quality training data and their performance hig...
Preprint
Full-text available
Vision transformer (ViT) has been trending in image classification tasks due to its promising performance when compared to convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViT models in hyperspectral image (HSI) classification tasks, but without achieving satisfactory performance. To this paper, we intro...
Article
In practice, the acquirement of labeled samples for hyperspectral image (HSI) is time-consuming and labor-intensive. It frequently induces the trouble of model overfitting and performance degradation for the supervised methodologies in HSI classification (HSIC). Fortunately, semisupervised learning can alleviate this deficiency, and graph convoluti...
Article
Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taki...
Article
Full-text available
Deep learning (DL) has aroused wide attention in hyperspectral unmixing (HU) owing to its powerful feature representation ability. As a representative of unsupervised DL approaches, autoencoder (AE) has been proven to be effective to better capture nonlinear components of hyperspectral images than the traditional model-driven linearized methods. Ho...
Article
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This paper proposes a denoising method based on sparse spectral-spatial and low-rank representations (SSSLRR) using 3-D orthogonal transform (3-DOT). SSSLRR can be effectively used to remove Gaussian and mixed noise. SSSLRR uses 3-DOT to decompose noisy HSI to sparse transform coefficients. 3-D discrete orthogonal wavelet transform (3-D DWT) is a r...
Chapter
Tensor representation is a feasible solution for analyzing large-volume, multirelational, and multimodal datasets, which are often conveniently represented as multiway arrays or tensors. It is therefore valuable and promising for the geoscience and remote sensing research communities to review tensor representation as an emerging tool for remote se...
Article
Full-text available
In this article, we propose a minimum simplex convolutional network (MiSiCNet) for deep hyperspectral unmixing. Unlike all the deep learning-based unmixing methods proposed in the literature, the proposed convolutional encoder–decoder architecture incorporates spatial information and geometrical information of the hyperspectral data in addition to...
Article
Full-text available
Due to advances in remote sensing satellite imaging and image processing technologies and their wide applications, intelligent remote sensing satellites are facing an opportunity for rapid development. The key technologies, standards, and laws of intelligent remote sensing satellites are also experiencing a series of new challenges. Novel concepts...
Article
Hyperspectral target detection can be described as locating targets of interest within a hyperspectral image based on prior information of targets. The complexity of actual scenes limits the performance of traditional statistical methods that rely on model assumptions, and traditional machine learning methods rely on mapping functions with limited...
Article
Model-based approaches to pansharpening still constitute a class of widely employed methods, thanks to their straightforward applicability to many problems, dispensing the user from time-consuming training phases. The injection scheme based on an accurate estimation (exploiting regression) of the relationship between the details contained in the pa...
Article
Standard hyperspectral (HS) pansharpening relies on fusion to enhance low-resolution HS (LRHS) images to the resolution of their matching panchromatic (PAN) images, whose practical implementation is normally under a stipulation of scale invariance of the model across the training phase and the pansharpening phase. By contrast, arbitrary resolution...
Article
Hyperspectral anomaly detection (AD) aims to detect objects significantly different from their surrounding background. Recently, many detectors based on autoencoder (AE) exhibited promising performances in hyperspectral AD tasks. However, the fundamental hypothesis of the AE-based detector that anomaly is more challenging to be reconstructed than b...
Article
Deep learning works normally in PolSAR image classification because the complex terrain scattering characteristic results in large intraclass differences and high interclass similarity. Deep metric learning (DML) aims to make the features keep a closer intraclass and a farther interclass distance. Therefore, we introduce DML and then propose an N-c...
Article
Owing to the powerful data representation ability of deep learning (DL) techniques, tremendous progress has been recently made in hyperspectral image (HSI) classification. Convolutional neural network (CNN), as a main part of the DL family, has been proven to be considerably effective to extract spatial-spectral features for HSIs. Nevertheless, its...
Article
Recently, deep learning-based methodologies have attained unprecedented performance in hyperspectral (HS) pansharpening, which aims to improve the spatial quality of HS images (HSIs) by making use of details extracted from the high-resolution panchromatic (HR-PAN) image. However, it remains challenging to incorporate the details into the pansharpen...
Article
Infrared objects acquired from a long-distance have small sizes and are easily submerged by a complex and variable background. The existing deep network detection framework suffers greatly from the feature spatial resolution loss caused by the networks’ depth and multiple downsampling operations, which is extremely detrimental for small object dete...
Article
In recent years, hyperspectral image classification (HSIC) has achieved impressive progress with emerging studies on deep learning models. However, the classification performance downgrades due to the limited number of annotated samples, especially for minority classes. Notably, the imbalanced data dilemma is familiar in remote sensing hyperspectra...
Article
Large-scene precise classification of multispectral images (MSIs) has become one of the hot topics in remote sensing field. MSIs usually have wide swath and a meter or even submeter level of spatial resolution, which make large-scene observation possible. However, the limited number of spectral bands leads to the confusion of land covers in classif...
Article
Full-text available
This paper presents the scientific outcomes of the 2022 Hyperspectral Pansharpening Challenge organized by the 12th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE WHISPERS 2022). The 2022 Hyperspectral Pansharpening Challenge aims at fusing a panchromatic image with hyperspectral data to get a high spa...
Article
Full-text available
Recently, the semi-supervised graph convolutional network (GCN) has been verified to be one of the most effective approaches for hyperspectral image classification (HSIC). However, constrained by the small sample size (SSS) of the training data and spectral uncertainty, the classification performance is remained to be further improved. Moreover, at...
Article
Due to the limitations of imaging systems, satellite hyperspectral imagery (HSI), which yields rich spectral information in many channels, often suffers from poor spatial resolution. HSI super-resolution (SR) refers to the fusion of high spatial resolution multispectral imagery (MSI) and low spatial resolution HSI to generate HSI that has both a hi...
Article
Traditional hyperspectral (HS) pansharpening aims at fusing a hyperspectral image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to g...
Preprint
Full-text available
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. In this paper, we propose a novel deep neural network architecture with level-domain based loss function for pansharpening by taking into a...
Article
The Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has been organizing the annual Data Fusion Contest (DFC) since 2006. The contest promotes the development of methods for extracting geospatial information from large-scale, multisensor, multimodal, and multitemporal data. It aim...
Article
Although the performance of pansharpening has been significantly improved by advanced deep-learning (DL) technologies in recent years, most DL-based methods fail to process multispectral (MS) images with arbitrary numbers of bands by a single model. Consequently, it is inevitable to train separate models for MS images with different numbers of band...
Preprint
Full-text available
Multimodal data provide complementary information of a natural phenomenon by integrating data from various domains with very different statistical properties. Capturing the intra-modality and cross-modality information of multimodal data is the essential capability of multimodal learning methods. The geometry-aware data analysis approaches provide...
Article
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Due to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image class...
Article
Full-text available
In this paper, we elaborate on the scientific outcomes of the 2021 Data Fusion Contest (DFC2021), which was organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society, on the subject of geospatial artificial intelligence (AI) for social good. The ultimate objective of the contest was to mod...
Article
Target decomposition features are the cornerstone of subsequent analyses for PolSAR images. Generally, adopting single or several decomposition algorithms limits the representation ability for original terrain characteristics. Using all the existing decomposition features, however, will definitely increase computational complexity. Besides, some fe...
Article
In recent years, enormous research has been made to improve the classification performance of single-modal remote sensing (RS) data. However, with the ever-growing availability of RS data acquired from satellite or airborne platforms, simultaneous processing and analysis of multimodal RS data pose a new challenge to researchers in the RS community....
Preprint
Full-text available
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification c...