Multiscale, multimodal, and multitemporal remote sensing

DESCRIPTION

REMOTE SENSING OF THE ENVIRONMENT:
MULTISCALE, MULTIMODAL, AND MULTITEMPORAL REMOTE SENSING

Climate change, biodiversity conservation, pollution detection and monitoring, and natural disaster mitigation (vulnerability assessment, early warning detection, crisis management, etc.) are key issues with a very significant societal impact. To address these challenges, new resources are becoming increasingly available. Satellite remote sensing is currently undergoing a technical revolution with the emergence and rapid development of very high-resolution sensors. It enables the observation of the planet with wide coverage and very high spatial resolution, at a reduced cost and with a very short revisit time. The various modalities available (optical data, radar, etc.) provide access to a wide range of physical characteristics on the ground. Alongside the growing availability of satellite-based remote sensing data, airborne platforms and light unmanned aerial vehicles (drones) offer a tremendous opportunity to provide additional, complementary information with great flexibility. As a result, unprecedented quantities of digital data are now available, spanning diverse scales and modalities. In order to fully exploit the Big Data described above, realize the potential offered by new generations of remote sensing sensors, and effectively meet critical needs, certain pivotal scientific and technical challenges must be addressed. This is the core objective of this Chair: advanced information processing methods will be developed to handle heterogeneous and massive sources of information. This includes cutting-edge deep learning techniques, manifold and graph-based representations, and data fusion.
At the intersection of remote sensing, geosciences, mathematics, machine learning, and signal and image processing, this project aims to bridge the gap between increasingly urgent needs and the tremendous potential offered by remote sensing technologies, in the broadest sense.

ACTIVITIES

Modeling and monitoring of plant biodiversity in the Alpine ecosystem,
 

  • Design of a CubeSat with embedded AI to monitor the environment, with support from Teledyne e2V (kickoff in March 2020 and Mission Definition Review completed in November 2020) (Principal Investigator: Mathieu Barthélémy, CSUG)
  • Organization of an international scientific challenge in partnership with SolarAid, HPE, the European and Italian Space Agencies, and the IEEE Geoscience and Remote Sensing Society (challenge to be held in 2021)
  • Methodological developments in AI and remote sensing data across a variety of contexts

CHAIR EVENTS

An international scientific challenge (detecting human settlements without access to electricity using AI and multimodal remote sensing) was a tremendous success (74 teams from around the world participated, submitting 3,452 entries). The challenge was organized in collaboration with the IEEE Geoscience and Remote Sensing Society, HPE, and SolarAid (a UK-based NGO). A scientific publication will be released in 2022, accompanied by a press release.

Organization of a special session on “Incorporating Physics into Deep Learning” at the 2020 IEEE IGARSS (originally scheduled to be held in Hawaii in July 2020 and eventually held online in September 2020).

Roundtable: “Technology for the Planet,” Transfo Festival (Grenoble, Sept. 2020)

 

RESEARCH TOPICS

Extraction of latent variables through common subspace learning, multimodal manifold learning and alignment, and graphs in very high dimensions.
  • This should result in dividing the information into a common (shared) part and a part specific to the modality (and/or scale, and/or date).
  • These components (shared and specific, respectively) should be combined to provide a complete representation and understanding.
  • This will make it possible to transfer knowledge (from one date to another, from one modality to another, etc.) or to reconstruct missing data from a set of incomplete acquisitions.
  • Special attention will be given to the processing and analysis of hyperspectral data.
  • Multimodal autoencoders, (very large) graph matching, (local) low-dimensional manifold models…

SELECTED LIST OF PUBLICATIONS

  • Antoine Roueff, Maryvonne Gerin, Pierre Gratier, François Levrier, Jérôme Pety et al. C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud. Astronomy and Astrophysics - A&A, EDP Sciences, 2021, 645 (A26), pp. 1–27.
  • Pierre Gratier, Jérôme Pety, Emeric Bron, Antoine Roueff, Jan H. Orkisz et al. "Quantitative inference of the H2 column densities from 3 mm molecular emission: a case study of Orion B★." *Astronomy and Astrophysics* (A&A), EDP Sciences, 2021, 645 (January 2021), pp. A27.
  • Le Guillarme, N. & Thuiller, W. (2021) TaxoNERD: deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature. Methods in Ecology and Evolution.
  • M. Behmanesh, P. Adibi, J. Chanussot, C. Jutten, and S. M. S. Ehsani, "Geometric Multimodal Learning Based on Local Signal Expansion for Joint Diagonalization," in IEEE Transactions on Signal Processing, vol. 69, pp. 1271–1286, 2021.
  • K. Zheng et al., "Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 3, pp. 2487–2502, March 2021.
  • Z. Wu, J. Sun, Y. Zhang, Z. Wei, and J. Chanussot, "Recent Developments in Parallel and Distributed Computing for Remotely Sensed Big Data Processing," in Proceedings of the IEEE, vol. 109, no. 8, pp. 1282–1305, Aug. 2021.
  • M. Wang, Q. Wang, J. Chanussot, and D. Li, "Mixed Noise Removal from Hyperspectral Images Based on Multidirectional Low-Rank Modeling and Spatial–Spectral Total Variation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 488–507, Jan. 2021.
  • N. Liu, L. Li, W. Li, R. Tao, J. E. Fowler, and J. Chanussot, "Hyperspectral Restoration and Fusion with Multispectral Imagery via Low-Rank Tensor Approximation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7817–7830, Sept. 2021.
  • B. Ren, Y. Zhao, B. Hou, J. Chanussot, and L. Jiao, "A Mutual Information-Based Self-Supervised Learning Model for PolSAR Land Cover Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 11, pp. 9224–9237, Nov. 2021.
  • R. Restaino, G. Vivone, P. Addesso, and J. Chanussot, "Hyperspectral Sharpening Approaches Using Satellite Multiplatform Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 578–596, Jan. 2021.
  • L.-J. Deng, G. Vivone, C. Jin, and J. Chanussot, "Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6995–7010, Aug. 2021.
  • S. Chlaily, M. D. Mura, J. Chanussot, C. Jutten, P. Gamba, and A. Marinoni, "Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 7, pp. 5598–5618, July 2021.
  • M. Wang, Q. Wang, J. Chanussot, and D. Hong, "l₀-l₁ Hybrid Total Variation Regularization and Its Applications to Mixed Noise Removal in Hyperspectral Images and Compressed Sensing," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7695–7710, Sept. 2021.
  • Z. Han, D. Hong, L. Gao, B. Zhang, and J. Chanussot, "Deep Half-Siamese Networks for Hyperspectral Unmixing," in IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 11, pp. 1996–2000, Nov. 2021.
  • D. Hong, L. Gao, J. Yao, B. Zhang, A. Plaza, and J. Chanussot, "Graph Convolutional Networks for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 7, pp. 5966–5978, July 2021.
  • D. Hong, J. Yao, D. Meng, Z. Xu, and J. Chanussot, "Multimodal GANs: Toward Crossmodal Hyperspectral–Multispectral Image Segmentation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 6, pp. 5103–5113, June 2021.
  • "Spectral Unmixing for Exoplanet Direct Detection in Hyperspectral Data," by J. Rameau, J. Chanussot, A. Carlotti, M. Bonnefoy, and P. Delorme, *Astronomy & Astrophysics*, 649 (2021), A143.
  • Le Guillarme, N., Hedde, M., & Thuiller, W. (2021). STWO: an ontology for soil food web reconstruction. S4BioDiv 2021: 3rd International Workshop on Semantics for Biodiversity.
  • Le Guillarme, N., Hedde, M., & Thuiller, W. (2021). Building a Trophic Knowledge Graph to Support Soil Food Web Reconstruction. S4BioDiv 2021: 3rd International Workshop on Semantics for Biodiversity.
  • Organization of a special session on “Incorporating Physics into Deep Learning” at the 2020 IEEE IGARSS. (Scheduled to be held in Hawaii in July 2020 and ultimately held online in September 2020.)
  • Jing Yao, Danfeng Hong (corresponding author), Jocelyn Chanussot, Deyu Meng, Xiaoxiang Zhu, Zongben Xu. “Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution”; European Conference on Computer Vision, ECCV 2020: Computer Vision – ECCV 2020, pp. 208–224
  • Joint and Progressive Subspace Analysis (JPSA) with Spatial–Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction. Danfeng Hong, IEEE Member; Naoto Yokoya, IEEE Member; Jocelyn Chanussot, IEEE Fellow; Jian Xu, IEEE Member; and Xiao Xiang Zhu, IEEE Senior Member
  • Eduardo Tusa, Jean-Matthieu Monnet, Jean-Baptiste Barré, Mauro Dalla Mura, Michele Dalponte et al. “Individual Tree Segmentation Based on Mean Shift and Crown Shape Model for Temperate Forests.” IEEE Geoscience and Remote Sensing Letters, IEEE—Institute of Electrical and Electronics Engineers, 2020, pp. 1–5.
  • Eduardo Tusa, Jean-Matthieu Monnet, Jean-Baptiste Barré, Mauro Dalla Mura, Jocelyn Chanussot. Fusion of lidar and hyperspectral data for semantic segmentation of forest tree species. International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences, Copernicus GmbH (Copernicus Publications), 2020, XLIII-B3-2020, pp. 487–494. 
  • Yang Xu, Zebin Wu, Jocelyn Chanussot, Pierre Comon, Zhihui Wei. “Nonlocal Coupled Tensor CP Decomposition for Hyperspectral and Multispectral Image Fusion.” IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2020, 58 (1), pp. 348–362. 
  • Jing Qin, Harlin Lee, Jocelyn Chi, Lucas Drumetz, Jocelyn Chanussot et al. “Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization.” IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2020, 
  • Lucas Drumetz, Jocelyn Chanussot, Christian Jutten, Wing-Kin Ma, Akira Iwasaki. “Spectral Variability-Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry.” IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2020, 
  • Lucas Drumetz, Jocelyn Chanussot, Christian Jutten. Spectral Unmixing: A Derivation of the Extended Linear Mixing Model from the Hapke Model. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 
  • Pierre Gratier, Jérôme Pety, Emeric Bron, Antoine Roueff, Jan H. Orkisz et al. Quantitative inference of H2 column densities from 3-mm molecular emission: A case study of Orion B. Astronomy and Astrophysics - A&A, EDP Sciences, 2020,
  • Antoine Roueff, Maryvonne Gerin, Pierre Gratier, François Levrier, Jérôme Pety et al. C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud: An analysis of the precision achievable when modeling spectral lines within the Local Thermodynamic Equilibrium approximation. Astronomy and Astrophysics - A&A, EDP Sciences, 2020, 645, pp. A26. 
  • Antoine Roueff, Maryvonne Gerin, Pierre Gratier, François Levrier, Jérôme Pety et al. C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud. Astronomy and Astrophysics - A&A, EDP Sciences, 2021, 645 (A26), pp. 1–27. 
  • Pierre Gratier, Jérôme Pety, Emeric Bron, Antoine Roueff, Jan H. Orkisz et al. "Quantitative inference of the H2 column densities from 3 mm molecular emission: a case study of Orion B★." *Astronomy and Astrophysics* (A&A), EDP Sciences, 2021, 645 (January 2021), pp. A27. 
  • Javier Marcello, Edurne Ibarrola-Ulzurrun, Consuelo Gonzalo-Martin, Jocelyn Chanussot, Gemine Vivone. Assessment of Hyperspectral Sharpening Methods for the Monitoring of Natural Areas Using Multiplatform Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (10), pp. 8208–8222.
  • Lucas Drumetz, Travis Meyer, Jocelyn Chanussot, Andrea Bertozzi, Christian Jutten. “Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms.” IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2019, 28 (7), pp. 3435–3450. 
  • Fahim Irfan Alam, Jun Zhou, Alan Wee-Chung Liew, Xiuping Jia, Jocelyn Chanussot et al. Conditional Random Fields and Deep Feature Learning for Hyperspectral Image Segmentation. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (3), pp. 1612–1628. 
  • Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu. “An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing.” IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2019, 28 (4), pp. 1923–1938. 
  • Edurne Ibarrola-Ulzurrun, Lucas Drumetz, Javier Marcello, Consuelo Gonzalo-Martin, Jocelyn Chanussot. “Hyperspectral Classification Through Unmixing Abundance Maps to Address Spectral Variability.” IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (7), pp. 4775–4788. 
  • Lin He, Jiawei Zhu, Jun Li, Antonio Plaza, Jocelyn Chanussot et al. HyperPNN: Hyperspectral Pansharpening via Spectrally Predictive Convolutional Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019, 12 (8), pp. 3092–3100. 
  • Lin He, Yizhou Rao, Jun Li, Jocelyn Chanussot, Antonio Plaza et al. “Pansharpening via Detail Injection-Based Convolutional Neural Networks.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019, 12 (4), pp. 1188–1204. 
  • Guillaume Tochon, Mauro Dalla Mura, Miguel Angel Veganzones, Thierry Géraud, Jocelyn Chanussot. "Braids of Partitions for the Hierarchical Representation and Segmentation of Multimodal Images." *Pattern Recognition*, Elsevier, 2019, 95, pp. 162–172. 
  • Qi Wang, Shaoteng Liu, Jocelyn Chanussot, Xuelong Li. Scene Classification Using Recurrent Attention on VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (2), pp. 1155–1167.
  • Peng Wang, Lei Zhang, Gong Zhang, Hui Bi, Mauro Dalla Mura et al. “Superresolution Land Cover Mapping Based on Pixel-, Subpixel-, and Superpixel-Scale Spatial Dependence Using a Pansharpening Technique.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019, pp. 1–17. 
  • Théo Masson, Mauro Dalla Mura, Marie Dumont, Jocelyn Chanussot. “Snow Cover Estimation From Image Time Series Based on Spectral Unmixing.” IEEE Geoscience and Remote Sensing Letters, IEEE—Institute of Electrical and Electronics Engineers, 2019, 16 (3), pp. 337–341. 
  • Théo Masson, Mauro Dalla Mura, Marie Dumont, Jocelyn Chanussot. “Snow Cover Estimation From Image Time Series Based on Spectral Unmixing.” IEEE Geoscience and Remote Sensing Letters, IEEE—Institute of Electrical and Electronics Engineers, 2019, 16 (3), pp. 337–341. 
  • Douglas Baptista de Souza, Jocelyn Chanussot, Anne-Catherine Favre, Pierre Borgnat. “An Improved Stationarity Test Based on Surrogates.” IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2019, 26 (10), pp. 1431–1435. 
  • Xin Wu, Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Wei Li et al. ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (7), pp. 5146–5158.
  • Danfeng Hong, Naoto Yokoya, Nan Ge, Jocelyn Chanussot, Xiao Xiang Zhu. Learnable Manifold Alignment (LeMA): A Semi-Supervised Cross-Modality Learning Framework for Land Cover and Land Use Classification. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2019, ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 147, pp. 193–205. 
  • Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu. “CoSpace: Common Subspace Learning From Hyperspectral-Multispectral Correspondences.” IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (7), pp. 4349–4359. 
  • Xin Wu, Danfeng Hong, Jocelyn Chanussot, Yang Xu, Ran Tao et al. “Fourier-Based Rotation-Invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection.” IEEE Geoscience and Remote Sensing Letters, IEEE—Institute of Electrical and Electronics Engineers, in press, pp. 1–5.
  • Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William Robson Schwartz, Jefersson Alex dos Santos. "Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks." IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (10), pp. 7503–7520. 
  • Peng Wang, Mauro Dalla Mura, Jocelyn Chanussot, Gong Zhang. “Soft-Then-Hard Super-Resolution Mapping Based on the Pansharpening Technique for Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019, 12 (1), pp. 334–344. 
  • Rocco Restaino, Gemine Vivone, Paolo Addesso, Jocelyn Chanussot. “A Pansharpening Approach Based on Multiple Linear Regression Estimation of Injection Coefficients.” IEEE Geoscience and Remote Sensing Letters, IEEE—Institute of Electrical and Electronics Engineers, 2019, pp. 1–5. 
  • Xun Liu, Chenwei Deng, Jocelyn Chanussot, Danfeng Hong, Baojun Zhao. StfNet: A Two-Stream Convolutional Neural Network for Spatiotemporal Image Fusion. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (9), pp. 6552–6564. 
  • Yang Xu, Zebin Wu, Jocelyn Chanussot, Zhihui Wei. “Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution.” IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2019, 28 (6), pp. 3034–3047. 
  • Gemine Vivone, Paolo Addesso, Jocelyn Chanussot. “A Combiner-Based Full-Resolution Quality Assessment Index for Pansharpening.” IEEE Geoscience and Remote Sensing Letters, IEEE—Institute of Electrical and Electronics Engineers, 2019, 16 (3), pp. 437–441. 
  • Gemine Vivone, Paolo Addesso, Rocco Restaino, Jocelyn Chanussot, Mauro Dalla Mura. “Pansharpening Based on Deconvolution for Multiband Filter Estimation.” IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (1), pp. 540–553. 
  • Zebin Wu, Wei Zhu, Jocelyn Chanussot, Yang Xu, Stanley Osher. “Hyperspectral Anomaly Detection via Global and Local Joint Modeling of Background.” IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2019, 67 (14), pp. 3858–3869. 
  • Javier Marcello, Edurne Ibarrola-Ulzurrun, Consuelo Gonzalo-Martin, Jocelyn Chanussot, Gemine Vivone. Assessment of Hyperspectral Sharpening Methods for the Monitoring of Natural Areas Using Multiplatform Remote Sensing Imagery; IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (10), pp. 8208–8222. 
Published on October 14, 2026
Updated on October 14, 2026