Physis: Sparse Signal Processing Technologies For HyperSpectral Systems

Physis: Sparse Signal Processing Technologies For HyperSpectral Systems

Develop, test, and evaluate novel signal processing technologies for real-time processing of hyperspectral data cubes

The objective of PHySIS is to develop, test, and evaluate novel signal processing technologies for real-time processing of hyperspectral data cubes.

Although hyperspectral sensors capture massive amounts of high-dimensional data, relevant information usually lies in a low-dimensional space. The aim of the project is to extend recent theoretical and algorithmic developments in the field of sparsity-enforcing recovery, compressive sensing, and matrix completion, in order to build and exploit sparse representations adapted to the hyperspectral signals of interest. It is envisaged that all three, temporal, spatial and spectral domains of hyperspectral data will be explored for sparse representations. Thus, sparsity in the data will be used not only to improve estimation performance, but also to mitigate the enormous computational burden needed to analyze hyperspectral data and leverage the development of real-time hyperspectral processing systems.

PHySIS project, started in 2015, focused on Bottom-up space technologies at low TRL and is funded by the European Commission under the H2020-COMPET-06-2014.


Recent advances in the fields of electronics and optics technology have permitted the design and development of sophisticated hyperspectral imaging sensors, which are able to capture the naturally occurring imaging spectra at a very high spatial resolution forming three-dimensional data cubes. In addition, it is envisaged that the next generation hyperspectral video cameras will have the ability to capture several hyperspectral data cubes per second, at almost video rates. Hyperspectral video sequences possessing high temporal, spatial, and spectral resolution will combine the advantages of both video and hyperspectral imagery. This unprecedented wealth of information poses a major challenge and necessitates the development of highly sophisticated signal processing systems. Addressing simultaneously the explosive growth of data dimensionality and the need to accurately determine the type and nature of the objects being imaged is a task that is not sufficiently treated currently by conventional statistical data analysis methods.

The general goal of the proposed PHySIS project is to investigate extensions of compressive signal representations and sparsity-enforcing recovery technologies for the acquisition, compression, restoration and understanding of hyperspectral data. We will particularly emphasize on robust and adaptive mathematical methods which are able to efficiently recover real-world hyperspectral data with specific constraints and noise/perturbation models which appear commonly in the field of astrophysics data processing and surveillance/security imaging.

The main scientific objectives of this project are as follows:

  • Objective 1 - Efficient hyperspectral image acquisition. Design and evaluation of novel hardware architectures and signal processing paradigms offering the ability to produce hyperspectral video (4D hypercubes) at high frame rates.
  • Objective 2 - Sparse representations and compression for multivariate data. Consider novel extensions of standard multiscale signal representations to the hyperspectral case, including different extensions of dictionary learning emphasizing on specific constraints.
  • Objective 3 - Sparse priors and restoration algorithms for hyperspectral data. Investigate sparsity-enforcing restoration technologies to tackle the basic linear inverse problems encountered in hyperspectral data recovery.
  • Objective 4 - Robust recovery of hyperspectral data. Investigate new ways to account for realistic models of noise/contamination sources, including signal-dependent noise, impulsive noise, and photon noise, by accounting for (joint)-sparse additive contaminants.
  • Objective 5 - Hyperspectral image understanding. Investigate novel, sparsity constrained spectral unmixing and clustering algorithms, allowing a better understanding and interpretation of the content of hyperspectral images, by suitably exploiting the spectral and the spatial dimensions.
  • Objective 6 - Demonstration and validation of an HSI system. Integration and evaluation of acquisition, compression, restoration and understanding of hyperspectral visible and IR images under realistic conditions (TRL 4-5).

Project Partners are Foundation for Research and Technology - Hellas (FORTH), GREECE; Commissariat à l'Énergie Atomique (CEA), FRANCE; National Observatory of Athens (NOA), GREECE; Interuniversitair Micro-Electronica Centrum VZW (IMEC), BELGIUM; Planetek Italia Srl.

Planetek Italia was in charge of the "Application scenarios and system requirements". This work package considers the overall hyperspectral imaging system aspects, including:

  • Develop the details of the targeted scenarios, the associated requirements, and the system architecture,
  • Define the specification of the system requirements for the application scenarios will necessarily be an iterative process.
  • Define and verify mechanisms for achieving and automatically adjusting quality of results at each part of the system,
  • Explore and define the metrics that will be used to examine quality of results at each and across stages and
  • Explore and define the interfaces that are required for providing quality feedback among stages.

Application Scenarios

HyperSpectral Imaging (HSI) systems can be exploited as a powerful analysis tool for applications in environment and ecology, aquaculture, forestry, agriculture, and geoscience. Because hyperspectral image analysis is applicable to a wide range of research topics this selection can be enlarged according to the user needs and new fields of applications can be developed.

HSI advantage over broadband sensors is its ability to detect molecular absorption and particle scattering signatures of constituents. The finer spectral resolution of a hyperspectral imager allows detection of surface materials, as well as inferences of biological and chemical processes. This capability plays an important role in addressing issues like sustainability and environment encompassing the following key areas: agriculture, forestry, geology/soil, coastal/inland waters, and environment.

The specific application scenarios, which will guide the testing and validation of a spaceborne HSI platform in the framework of PHySIS, have been identified as:

  • Land monitoring for
    • a. Agricultural (soil chemical composition);
    • b. Forestry purposes (tracking changes in natural or planted forest coverage);
  • Soil mineral and texture monitoring
  • Coastal and inland waters monitoring.

Concerning the case of terrestrial application scenarios, the outcome of the PHySIS project could be exploited for:

  • Recycling industry;
  • Precision agriculture (e.g. early detection of plant diseases);
  • Food processing (e.g. internal/external quality metrology, microbe detection);
  • Medical applications (diagnosis, dermatology);
  • Biomedical/micrology (biological sample’s emission or reflectance spectrum, containing important structural, biochemical, and physiological information);
  • Forensics (e.g. criminology, acquisition process & analysis).

Methods and quality metrics are defined to assess the performance of a given system and that will be used to optimize the system. These metrics consider all the individual steps involved including the complexity and cost of the fabrication process for novel hyperspectral imaging sensors, the imaging capabilities of considered systems with respect to spatial, spectral and temporal resolution and integration capabilities with other components. The evaluation targets both space and terrestrial applications.

In this context, trade-offs between metrics such as weight, size, cost, power consumption, throughput, spatial and spectral resolution are being evaluate.

project information

European Commission under the H2020-COMPET-06-2014
Application fields: