Jocelyn Chanussot

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

Remote Sensing (RS) data contains a wealth of multi-dimensional information crucial for Earth observation. Owing to its vast volume, diverse sources, and temporal properties, RS data is highly suitable for the development of large Visual Foundation Models (VFMs). VFMs act as robust feature extractors, learning from extensive RS data, and are subseq...

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification owing to its promising feature learning and representation capabilities. However, limited by the spatial resolution of sensors, existing DL-based classification approaches mainly focus on pixel-level spectral and spatial information extraction through complex n...

Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce hand-crafted cost in the field of remote sensing. However, existing approaches focus on single-source domain generalization to unseen target domains, and are easily confused by large real-world domain shi...

Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce hand-crafted cost in the field of remote sensing. However, existing approaches focus on single-source domain generalization to unseen target domains, and are easily confused by large real-world domain shi...

Multi-modal image fusion aims to generate a fused image by integrating and distinguishing the cross-modality complementary information from multiple source images. While the cross-attention mechanism with global spatial interactions appears promising, it only captures second-order spatial interactions, neglecting higher-order interactions in both s...

The most cost-effective way to obtain a high spatial resolution hyperspectral image (HrHSI) is to fuse a low spatial resolution hyperspectral image (LrHSI) and corresponding high spatial resolution multispectral image (HrMSI). This article proposes a generalizable unsupervised deep fusion method based on spectral-spatial collaborative constraint to...

Context. Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying t...

Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying to assess...

Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potential in hyperspectral imaging (HSI) remains untapped due to the absence of comprehensive and globally representative hyperspectral datasets. To close this gap, we introduc...

In recent years, Transformers have garnered significant attention for Hyperspectral Image Classification (HSIC) due to their self-attention mechanism, which provides strong classification performance. However, these models face major challenges in computational efficiency, as their complexity increases quadratically with the sequence length. The Ma...

Natural disasters commonly occur in all regions around the world and cause huge financial and human losses. One of the main effects of earthquakes and floods is the destruction of buildings. Photogrammetric and remote sensing (RS) data track changes and detect damages in these events. Considering the evolution in deep learning (DL) techniques, the...

Hyperspectral pansharpening consists of fusing a high-resolution panchromatic band and a low-resolution hyperspectral image to obtain a new image with high resolution in both the spatial and spectral domains. These remote sensing products are valuable for a wide range of applications, driving ever growing research efforts. Nonetheless, results stil...

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification owing to its promising feature learning and representation capabilities. However, limited by the spatial resolution of sensors, existing DL-based classification approaches mainly focus on pixel-level spectral and spatial information extraction through complex n...

Multispectral image (MS) and panchromatic image (PAN) fusion, which is also named as multispectral pansharpening, aims to obtain MS with high spatial resolution and high spectral resolution. However, due to the usual neglect of noise and blur generated in the imaging and transmission phases of data during training, many deep learning (DL) pansharpe...

Accurately distinguishing between background and anomalous objects within hyperspectral images poses a significant challenge. The primary obstacle lies in the inadequate modeling of prior knowledge, leading to a performance bottleneck in hyperspectral anomaly detection (HAD). In response to this challenge, we put forth a groundbreaking coupling par...

【❗❗❗❗❗❗我的微信是BatAug,欢迎交流合作❗❗❗❗❗❗. If you meet any problems, welcome to contact me via WeChat:BatAug】 By fusing a low-resolution hyperspectral image (LrMSI) with an auxiliary high-resolution multispectral image (HrMSI), hyperspectral image super-resolution (HISR) can generate a high-resolution hyperspectral image (HrHSI) economically. Despite the pro...

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. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which of...

Due to the complexity of the areas and the diversity of the objects, traditional Burned Area Mapping (BAM) methods cannot provide promising results. Moreover, these methods focus on additional processing to improve their results, which is time-consuming and complex. Therefore, an advanced framework is needed to achieve accurate results in burned ar...

multimodal image fusion involves tasks like pan-sharpening and depth super-resolution. Both tasks aim to generate high-resolution target images by fusing the complementary information from the texture-rich guidance and low-resolution target counterparts. They are inborn with reconstructing high-frequency information. Despite their inherent frequenc...

This paper proposes a novel method for robot navigation in high-dimensional environments that reduce the dimension of the state space using local and soft feature selection. The algorithm selects relevant features based on local correlations between states, avoiding duplicate inappropriate information and adjusting sensor values accordingly. By opt...

Hyperspectral anomaly detection aims at distinguishing targets of interest from the background without prior knowledge. Although low-rank representation (LRR) based methods have been broadly applied in anomaly detection tasks, how to approximate the penalties in LRR-based methods more precisely is still a problem that needs to be further investigat...

Model-based target decomposition method has been widely applied due to its clear physical scattering significance. However, after establishing decomposition basis, the process of solving the scattering components and parameters is usually underdetermined, which will lead to the issues such as component negative power and overestimation. For this pr...

Due to the high data demand of machine learning algorithms, multiple datasets are emerging in remote sensing. But these datasets are costly and time consuming to annotate especially for change detection or natural phenomena monitoring. In particular, early warning systems on slow-moving disasters are lacking of training datasets as they require bot...

In recent years, deep learning has emerged as the dominant approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize well. This limits the applicability of deep learning for real-world HSI classification problems, as manual labeling of thousands of pixels per scene is costly...

In large-scale disaster events, the planning of optimal rescue routes depends on the object detection ability at the disaster scene, with one of the main challenges being the presence of dense and occluded objects. Existing methods, which are typically based on the RGB modality, struggle to distinguish targets with similar colors and textures in cr...

Standard hyperspectral (HS) pansharpening utilizes panchromatic (PAN) images to improve the connected low-resolution HS (LRHS) images to the spatial resolutions of PANs; while arbitrary-resolution hyperspectral (ARHS) pansharpening aims to use PANs to enhance LRHS images to any desired spatial resolutions. For the challenging task of ARHS pansharpe...

The spectral super-resolution (SpeSR) from multispectral images (MSIs) to hyperspectral images (HSIs) can bring rich spectral information. The deep learning-based methods have demonstrated their powerful ability for the SpeSR task, which requires the paired HSI/MSI to train the model. However, HSIs and MSIs are always obtained at different times an...

Sentinel-5P provides excellent spatial information, but its resolution is insufficient to characterise the complex distribution of air contaminants within limited areas. As physical constraints prevent significant advances beyond its nominal resolution, employing processing techniques like single-image super-resolution can notably contribute to bot...

Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers,...

Given the ever-growing availability of remote sensing data (e.g., Gaofen in China, Sentinel in the EU, and Landsat in the USA), multimodal remote sensing techniques have been garnering increasing attention and have made extraordinary progress in various Earth observation (EO)-related tasks. The data acquired by different platforms can provide diver...

Extracting rock objects from the surface of celestial bodies in deep space exploration environments is crucial for self-service path planning, navigation of detectors, and regional information evaluation. Most existing image saemantic segmentation frameworks decrease the spatial resolution of the feature maps as networks deepen, resulting in limita...

Segment anything model (SAM) has been widely applied to various downstream tasks for its excellent performance and generalization capability. However, SAM exhibits three limitations related to remote sensing (RS) semantic segmentation task: 1) the image encoders excessively lose high-frequency information, such as object boundaries and textures, re...

Recently, cross-scene hyperspectral image classification (HSIC) has attracted increasing attention, alleviating the dilemma of no labeled samples in the target domain (TD). Although collaborative source and target training has dominated this field, training effective feature extractors and overcoming intractable domain gaps remain challenging. To c...

In recent years, remote sensing (RS) image scene classification methods have experienced notable development due to the powerful feature extraction ability of deep learning. However, current methods for RS image scene classification (RSSC) tasks struggle with handling unseen scene categories because of their reliance on large amounts of high-qualit...

Zero-shot classification models aim to recognize image categories that are not included in the training phase by learning seen scenes with semantic information. This approach is particularly useful in remote sensing since it can identify previously unseen classes. However, most zero-shot remote sensing scene classification approaches focus on match...

Current hyperspectral anomaly detection (HAD) benchmark datasets suffer from low resolution, simple background, and small size of the anomalies. 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 paramet...

In data acquisition and transmission, hyperspectral (HS) images are inevitably corrupted by additive noises, making it challenging to accurately observe and recognize the materials on the surface of the Earth. However, scholars have been addicted to developing numerous complex methods for separable two-stage denoising and anomaly detection (AD) tas...

In this article, we introduce a novel linear model tailored for semisupervised/library-based unmixing. Our model incorporates considerations for library mismatch while enabling the enforcement of the abundance sum-to-one constraint (ASC). Unlike conventional sparse unmixing methods, this model involves nonconvex optimization, presenting significant...

Land use/land cover classification with multimodal data has attracted increasing attention. For hyperspectral images (HSI) and light detection and ranging (LiDAR) data, the combination of them can make the classification more accurate and robust. However, how to effectively utilize their respective strengths and integrate them with the classificati...

Multi-modal remote sensing image registration is crucial for multi-modal information fusion and applications. The significant non-linear appearance difference between multi-modal images caused by the various imaging mechanisms dramatically increases the challenge of image registration. This paper proposes an adaptive frequency feature filtering net...

Few-shot learning (FSL) has rapidly advanced in the hyperspectral image (HSI) classification, potentially reducing the need for laborious and expensive labeled data collection. Due to the limited receptive field, the convolutional neural network (CNN) struggles to capture long-range dependencies for extracting global features. Additionally, the tra...

Gathering extensive labeled data during Mars missions is costly and unrealistic, especially considering the complex and unpredictable Martian environment where new and unfamiliar scenes may emerge. Traditional Mars scene classification methods depend heavily on large amounts of labeled data, which makes it impractical to recognize previously unseen...

Classifiers trained on airborne hyperspectral imagery are proficient in identifying tree species in hyperdiverse tropical rainforests. However, spectral fluctuations, influenced by intrinsic and environmental factors, such as the heterogeneity of individual crown properties and atmospheric conditions, pose challenges for large-scale mapping. This s...

Existing cross-domain few-shot learning (FSL) methods for hyperspectral image (HSI) classification have garnered widespread attention due to their excellent performance in recognizing novel classes. To mitigate domain shift, researchers focus on designing sophisticated domain adaptation (DA) modules to directly apply biased meta-knowledge in target...

In recent years, multimodal remote sensing data classification (MMRSC) has evoked growing attention due to its more comprehensive and accurate delineation of Earth’s surface compared to its single-modal counterpart. However, it remains challenging to capture and integrate local and global features from single-modal data. Moreover, how to fully exca...

Unsupervised domain adaptation reduces domain shifts between distributions to enable model generalization to new scenarios. Adversarial domain adaptation is an effective approach that extracts domain-invariant features through adversarial learning, but such methods often neglect the influence of category differences on domain discrimination. To sol...

Deep feature learning methods have shown significant advantages over handcrafted feature-based methods in remote sensing image matching and registration. Existing deep learning methods usually introduce complex modules into the deep convolutional network for more robust feature learning. However, they usually require high computation and memory res...

Image fusion can be conducted at different levels, with pixel-level image fusion involving the direct combination of original information from source images. The objective of methods falling under this category is to generate a fused image that enhances both visual perception and subsequent processing tasks. This survey draws upon research findings...

Nowadays, various graph convolutional networks (GCNs) to process graph-structured data have been proposed for hyperspectral image (HSI) classification. Nevertheless, most GCN-based HSI classification methods emphasize graph node feature aggregation instead of graph pooling, resulting in them being shallow networks and unable to extract deep discrim...

Hyperspectral (HS) pansharpening consists of fusing a high-resolution panchromatic (PAN) band and a low-resolution HS image to obtain a new image with high resolution in both the spatial and spectral domains. These remote sensing products are valuable for a wide range of applications, driving ever-growing research efforts. Nonetheless, results stil...

Local and global spectral and spatial information is crucial for hyperspectral image (HSI) classification. However, modeling the global context has been challenging due to the limitations of receptive fields and quadratic complexity. Mamba’s ability to leverage long-range dependencies with linear computational complexity offers an effective approac...

3D building change detection (CD) methods detect more accurate multiple change maps than 2D ones. Recent technologies such as unmanned aerial vehicle (UAV) systems and dense image matching have made it much easier to obtain 3D data nowadays. Developing a solution which produces an accurate map of multiple building changes, including Unclassified,...

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. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which of...

The synthesis of high-resolution (HR) hyperspectral image (HSI) by fusing a low-resolution HSI with a corresponding HR multispectral image has emerged as a prevalent HSI super-resolution (HSR) scheme. Recent researches have revealed that tensor analysis is an emerging tool for HSR. However, most off-the-shelf tensor-based HSR algorithms tend to enc...

In recent years, massive efforts have been made to improve the spatial resolution of hyperspectral (HS) images with the assistance of other high-spatial-resolution (HR) imaging sources like RGB and multispectral sensors. Convolutional neural network (CNN)-based techniques are widely used to solve the HS image super-resolution (HISR) problem, but CN...

Context. The interpretation of observations of atomic and molecular tracers in the galactic and extragalactic interstellar medium (ISM) requires comparisons with state-of-the-art astrophysical models to infer some physical conditions. Usually, ISM models are too timeconsuming for such inference procedures, as they call for numerous model evaluation...

We present 5 deg^2 (~250 pc^2) HCN, HNC, HCO+, and CO J=1-0 maps of the Orion B GMC, complemented with existing wide-field [CI] 492 GHz maps, as well as new pointed observations of rotationally excited HCN, HNC, H13CN, and HN13C lines. We detect anomalous HCN J=1-0 hyperfine structure line emission almost everywhere in the cloud. About 70% of the t...

Timely and accurate building damage mapping is essential for supporting disaster response activities. While RS satellite imagery can provide the basis for building damage map generation, detection of building damages by traditional methods is generally challenging. The traditional building damage mapping approaches focus on damage mapping based on...

The interpretation of observations of atomic and molecular tracers in the galactic and extragalactic interstellar medium (ISM) requires comparisons with state-of-the-art astrophysical models to infer some physical conditions. Usually, ISM models are too time-consuming for such inference procedures, as they call for numerous model evaluations. As a...

Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers,...

This paper introduces a new sparse unmixing technique using archetypal analysis (SUnAA). First, we design a new model based on archetypal analysis. We assume that the endmembers of interest are a convex combination of endmembers provided by a spectral library and that the number of endmembers of interest is known. Then, we propose a minimization pr...

Convolutional neural networks (CNNs) have recently achieved outstanding performance for hyperspectral (HS) and multispectral (MS) image fusion. However, CNNs cannot explore the long-range dependence for HS and MS image fusion because of their local receptive fields. To overcome this limitation, a transformer is proposed to leverage the long-range d...

Context. The availability of large bandwidth receivers for millimeter radio telescopes allows for the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain a lot of information on the physical, chemical, and kinematical properties of the emitting gas. However, their large...

Context. The availability of large bandwidth receivers for millimeter radio telescopes allows the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain much information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size co...

Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared with convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViTs in hyperspectral image (HSI) classification tasks. To achieve satisfactory performance, close to that of CNNs, transforme...