Hyperspectral Image Acquisition: We designed and developed novel approaches for HSI acquisition. Exploiting the capabilities of the SSI technology, innovative mathematical models were developed in order to maximize the quality of the acquired imagery. To that end, three reference SSI architectures were employed, including a linescan, a 4x4 mosaic and a 5x5 mosaic. At the same time, low-rank and sparse modeling algorithmic paradigms were introduced in order to increase spatial and spectral resolution.
Sparse Representation and Compression of Hyperspectral Data: We investigated and proposed novel signal representation techniques appropriate for the compression of hyperspectral data, in conjunction with their implementation on future customized platforms. To this end, the PHySIS prototype platform was designed for jointly acquiring and compressing hyperspectral data, following the premises of compressed sensing and matrix completion. Most importantly, the developed platform, which wss built on top of an advanced hardware module (NVIDIA’s Jetson TK1 system), integrated a sophisticated lightweight encoder and a high performance decoder, in alignment with current and future HSI systems.
Sparsity-enforcing Restoration and Robust Recovery: We investigated strategies for the robust recovering of hyperspectral data, including the development of new methods which account for the presence of realistic models like impulsive noise and signal-dependent noise. To that end, various penalization schemes were explored including low-rank and spare models. The developed framework was introduced as a formal approach for the problem of Blind Source Separation which was evaluated on ESA-Planck simulated data.
Hyperspectral Image Understanding: Unmixing, Clustering, and Joint Unmixing/Clustering: Spectral unmixing is the process of separating the spectrum of a mixed measured pixel into its constituent components; the spectra of pure materials, also called endmembers, and their corresponding fractional proportions in each pixel, called abundances. We developed spectral unmixing algorithms that go beyond state-of-the-art, by exploiting not only the spectral but also the inherent spatial information of hyperspectral images. Furthermore, two novel possibilistic clustering algorithms were developed, which utilize the concept of sparsity, along with a new online clustering scheme. Last, the potential of combining the unmixing and clustering procedures was investigated with the aim to further improve clustering performance.
Integration, Demonstration, and Validation: We considered the theoretical results and prototyped algorithms of all SW processing for hyperspectral data handling to develop the engineered system integrating all the partial results. It focused on the sensing/acquisition and the novel compression/restoration/understanding techniques developed during the project, acting as the integration point of the overall research activities to obtain a complete system able to demonstrate in an end-to-end configuration the proposed functionalities and performances.
PHySIS achieved full spectral image estimates using compressed sensing reconstruction methods to process observations collected using an innovative system design. Employing novel sparse representations and matrix completion principles, we were able to accurately reconstruct spectral images with an order of magnitude more reconstructed voxels than measurements. PHySIS proposed computational hyperspectral sensor design principles will allow the next generation of hyperspectral systems to be made dramatically smaller, thus reducing their overall cost, and spark a new line of long-term technology in remote sensing and earth observation.
