A feasability study for a compressive sensing imager in the medium infrared for hotspot detection

In this paper, we propose an innovative concept for an optical payload for Earth Observation, which operates in the medium infrared, based on two emerging technological approaches: super-resolution and compressive sensing. The aim is to improve payload performances in terms of ground spatial resolution and mitigation of some effects, such as saturation and blooming, that are often a limit for obtaining high quality level products in many application domains, such as the detection and monitoring of fire, lava, and, more generally, hotspots. Both approaches are based on the use of a Spatial Light Modulator (SLM), an optoelectronic device consisting of an array of micro-mirrors electronically actuated. The main advantages of the proposed concept consist in: (1) increased ground spatial resolution with respect to the number of pixels of the detector used; (2) expected mitigation of the blooming and saturation effects of the single pixel when high temperature hotspots are observed; (3) compressed-format capture typical of compressive sensing, which eliminates the need for a separate compression card, saving mass, memory and energy consumption.

[1]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[2]  Malvina Silvestri,et al.  A Sensitivity Study of the 4.8 µm Carbon Dioxide Absorption Band in the MWIR Spectral Range , 2020, Remote. Sens..

[3]  Abhijit Mahalanobis,et al.  High-resolution imaging using a translating coded aperture , 2017 .

[4]  Y. Kaufman,et al.  Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release , 2005 .

[5]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[6]  Waheed U. Bajwa,et al.  From modeling to hardware: an experimental evaluation of image plane and Fourier plane coded compressive optical imaging , 2017 .

[7]  Enrico Magli,et al.  OPTICAL COMPRESSIVE SENSING TECHNOLOGIES FOR SPACE APPLICATIONS: A PROS AND CONS ANALYSIS OF APPLICATION-DRIVEN INSTRUMENTAL CONCEPTS , 2016 .

[8]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[9]  A. Mahalanobis,et al.  Recent results of medium wave infrared compressive sensing. , 2014, Applied optics.

[10]  Jian Wang,et al.  Recovery of Sparse Signals via Generalized Orthogonal Matching Pursuit: A New Analysis , 2013, IEEE Transactions on Signal Processing.

[11]  Maria Fabrizia Buongiorno,et al.  Error analysis of subpixel lava temperature measurements using infrared remotely sensed data , 2012 .

[12]  Bertrand Chapron,et al.  ESA's Living Planet Programme: Scientific Acheivements and Future Challenges – Scientific Context of Earth Observation Science Strategy for ESA , 2015 .

[13]  C. Lastri,et al.  Compressive sensing for hyperspectral earth observation from space , 2017, International Conference on Space Optics.

[14]  Aswin C. Sankaranarayanan,et al.  FPA-CS: Focal plane array-based compressive imaging in short-wave infrared , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Bernard Ghanem,et al.  ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[16]  Daniela Coltuc,et al.  Optical compressive sensing technologies for space applications: instrumental concepts and performance analysis , 2019, International Conference on Space Optics.

[17]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.