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Sparse Signal Processing Technologies for HyperSpectral Imaging Systems

Deliverables

Space market analysis

This deliverable will be focused on ways to promote the technologies developed in PHySIS in future hyperspectral imaging space missions by presenting the outcomes of the research to appropriate associations and commissions including the European Remote Sensing Companies Association and the European panel of space SMEs Association. Exploitation plans drafted by the consortium will include a review on the position of the project with respect to specific market and preliminary exploitation strategies for both individual modules and end-to-end system design.

Report on the analysis and evaluation of video SSI architectures

A report on the evaluation of various HSI technologies against native spectral, spatial resolution, compactness and cost, focusing especially on technologies capable of generating hyperspectral video. Strengths and weakness of each of the approaches will be highlighted. This deliverable will provide valuable input to the rest of the project activities (compression, spatial/spectral resolution enhancement) that will result in high performance and compact snapshot video systems.

User manual and demonstration procedures

A report on the demonstration activities providing the necessary support to the WP 8 for the dissemination activities to the potential customer and any users. The deliverable will include a storyboard to use the system in the demonstration activities providing any input products and steps to follow to demonstrate the functionalities of the overall system.

Scenario descriptions and system requirements (v2)

Develop the details of the targeted scenarios, the associated requirements, and the system architecture. This deliverable will be the updated version of the initial release, which appeared in M3.

Data management plan

An organized strategy about the exploitation, sharing, access, verification, and reuse of the PHySIS produced hyperspectral data.

Report on the robust recovery of hyperspectral data

Report on the design of hyperspectral data recovery methods when the instrumental noise to be modeled is typical of optical systems. This includes shot noise as well as outliers or more generally sparse noise. In this task, the sparsity of the hyperspectral data to be retrieved will be enforced using standard signal representations such as wavelets. We will make use of recent advances in optimization - and more precisely proximal calculus - for the design of the recovery algorithm.

Risk assessment procedure

Description of the establishment of necessary procedures for risk assessment. Important risks will be monitored and mitigation actions will be implemented where relevant.

Report on efficient hyperspectral image compression

Report on novel approaches in hyperspectral image compression, leveraging the computational power of sparse and low rank representations. The proposed compression algorithm will be evaluated both in terms of compression performance as well as computational complexity. More specifically, we will investigate (i) the spatio-temporal characteristics of hyperspectral images, (ii) methods for progressive spatio-temporal multiplexing of hyperspectral cubes, (iii) channel encoding of multiplexed cubes for efficient and robust transmission and (iv) reconstruction of the hyperspectral cubes from the received messages.

Report on the application scenarios

Specify application scenarios and describe the operational tasks that cannot be addressed with classical single-band visible or infrared cameras. Study and explain how the operational tasks can be tackled by hyperspectral systems. Emphasize the hyperspectral design parameters that will play an important role in the overall performance of the system.

Annual report on dissemination activities (Y1)

A report on the actual implemented ways of communicating the results of the various PHYSIS WPs to the research community and to different special interest groups during Year 1.

Terrestrial markets analysis

This deliverable will consider the opportunities for the exploitation of PHySIS findings in terrestrial applications including security, food, agriculture and archeology.

Annual report on dissemination activities (Y2)

A report on the actual implemented ways of communicating the results of the various PHYSIS WPs to the research community and to different special interest groups during Year 2.

Report on unmixing algorithms for hyperspectral data

A report on the development of new hyperspectral unmixing algorithms that go beyond the state-of-the-art in two respects. First, the inherent spatial resolution existing very often in hyperspectral images will be exploited and properly incorporated in the devised schemes. Second, various nonlinear mixing models will be investigated and unmixing algorithms adjusted to these models will be sought. The compressive sensing and Bayesian statistical frameworks will be used as the basis of our developments, enabling the design of sparsity aware statistical algorithms addressing the previously mentioned issues.

Scenario descriptions and system requirements (v1)

Develop the details of the targeted scenarios, the associated requirements, and the system architecture. The detailed scenario descriptions and the corresponding requirements will be the basis for the work done in the WPs 3-7 and will determine many system requirements such as imaging modalities, type and spatial-spectral-temporal density of measurement data depending on the specific scenario, spatial extension to be monitored, necessary co-operative tasks, and overall modular architecture of the system. The initial release of this deliverable will happen in M3.

Publications

Deep Convolutional Neural Networks for the Classification of Snapshot Mosaic Hyperspectral Imagery




Detecting hyperplane clusters with adaptive possibilistic clustering

Author(s): K. D. Koutroumbas, S. D. Xenaki, A. A. Rontogiannis
Published in: Proceedings of the 9th Hellenic Conference on Artificial Intelligence - SETN '16, 2016, Page(s) 1-7
DOI: 10.1145/2903220.2903236

A Self-Similar and Sparse Approach for Spectral Mosaic Snapshot Recovery

Author(s): G. Tsagkatakis and P. Tsakalides
Published in: Proc. 2016 IEEE International Conference on Imaging Systems and Techniques (IST 2016), 2016

Lightweight Onboard Hyperspectral Compression and Recovery by Matrix Completion

Author(s): G. Tsagkatakis, L. Amoruso, D. Sykas, C. Abbattista, and P. Tsakalides
Published in: Proc. 5th International Workshop on On-Board Payload Data Compression (OBPDC 2016), 2016

Joint Deconvolution and Blind Source Separation of Hyperspectral Data Using Sparsity

Author(s): M. Jiang, J.-L. Starck, J. Bobin M. Jiang, J.-L. Starck, J. Bobin, “Joint Deconvolution and Blind Source Separation of Hyperspectral Data Using Sparsity”, in SIAM Conference on Imaging Science, Albuquerque, New Mexico, May 23-26, 2016
Published in: SIAM Conference on Imaging Science, 2016

Deep Feature Learning for Hyperspectral Image Classification and Land Cover Estimation

Author(s): G. Tsagkatakis and P. Tsakalides
Published in: Living Planet Symposium, Issue 740, 2016

Spectral Super-Resolution for Hyperspectral Images via Sparse Representations

Author(s): Konstantina Fotiadou, Grigorios Tsagkatakis and Panagiotis Tsakalides
Published in: Living Planet Symposium, Issue 740, 2016

Sparse BSS in the presence of outliers

Author(s): C. Cécile, J. Bobin, and J. Rapin
Published in: SPARS, 2015

Compressed sensing and radio interferometry

Author(s): M. Jiang, J. N. Girard, J.-L. Starck, S. Corbel, C. Tasse
Published in: 2015 23rd European Signal Processing Conference (EUSIPCO), 2015, Page(s) 1646-1650
DOI: 10.1109/EUSIPCO.2015.7362663

Non-negative Matrix Completion for the Enhancement of Snapshot Mosaic Multispectral Imagery




Spectral Resolution Enhancement of Hyperspectral Images via Sparse Representations




Compressed Hyperspectral Sensing




Sparsity and inverse problems in astrophysics

Author(s): Jean-Luc Starck
Published in: Journal of Physics: Conference Series, Issue 699, 2016, Page(s) 012010, ISSN 1742-6588
DOI: 10.1088/1742-6596/699/1/012010

Online sparse and low-rank subspace learning from incomplete data: A Bayesian view

Author(s): Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos E. Themelis, Konstantinos D. Koutroumbas
Published in: Signal Processing, Issue 137, 2017, Page(s) 199-212, ISSN 0165-1684
DOI: 10.1016/j.sigpro.2017.02.003

Constraint matrix factorization for space variant PSFs field restoration

Author(s): F Ngolè, J-L Starck, K Okumura, J Amiaux, P Hudelot
Published in: Inverse Problems, Issue 32/12, 2016, Page(s) 124001, ISSN 0266-5611
DOI: 10.1088/0266-5611/32/12/124001

Sparsity-Aware Possibilistic Clustering Algorithms

Author(s): Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
Published in: IEEE Transactions on Fuzzy Systems, Issue 24/6, 2016, Page(s) 1611-1626, ISSN 1063-6706
DOI: 10.1109/TFUZZ.2016.2543752

Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing

Author(s): Paris V. Giampouras, Konstantinos E. Themelis, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Published in: IEEE Transactions on Geoscience and Remote Sensing, Issue 54/8, 2016, Page(s) 4775-4789, ISSN 0196-2892
DOI: 10.1109/TGRS.2016.2551327

Variational Bayes Group Sparse Time-Adaptive Parameter Estimation With Either Known or Unknown Sparsity Pattern

Author(s): Konstantinos E. Themelis, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Published in: IEEE Transactions on Signal Processing, Issue 64/12, 2016, Page(s) 3194-3206, ISSN 1053-587X
DOI: 10.1109/TSP.2016.2543204

Characterization of VNIR Hyperspectral Sensors with Monolithically Integrated Optical Filters

Author(s): Prashant Agrawal, Klaas Tack, Bert Geelen, Bart Masschelein, Pablo Mateo Aranda Moran, Andy Lambrechts, Murali Jayapala
Published in: Electronic Imaging, Issue 2016/12, 2016, Page(s) 1-7, ISSN 2470-1173
DOI: 10.2352/ISSN.2470-1173.2016.12.IMSE-280

Land Classification Using Remotely Sensed Data: Going Multilabel

Author(s): Konstantinos Karalas, Grigorios Tsagkatakis, Michael Zervakis, Panagiotis Tsakalides
Published in: IEEE Transactions on Geoscience and Remote Sensing, Issue 54/6, 2016, Page(s) 3548-3563, ISSN 0196-2892
DOI: 10.1109/TGRS.2016.2520203

Joint Multichannel Deconvolution and Blind Source Separation

Author(s): M. Jiang, J. Bobin and J.-L. Starck
Published in: SIAM Journal on Imaging Sciences, 2017, ISSN 1936-4954

Space variant deconvolution of galaxy survey images


Published in: ISSN 0004-6361
DOI: 10.1051/0004-6361/201629709

Spectral Unmixing-Based Clustering of High-Spatial Resolution Hyperspectral Imagery

Author(s): Eleftheria A. Mylona, Olga A. Sykioti, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, Page(s) 1-11, ISSN 1939-1404
DOI: 10.1109/JSTARS.2017.2687703

PSFs field learning based on Optimal Transport distances

Author(s): F.M. Ngolè Mboula and J.-L. Starck
Published in: SIAM Journal Imaging Science, 2017, ISSN 1936-4954

Computational Snapshot Spectral Imaging

Author(s): Grigorios Tsagkatakis and Panagiotis Tsakalides
Published in: ERCIM News, Issue 108, 2017, Page(s) 39