Do we miss targets when we capture hyperspectral images with compressive sensing?

Noam Katz, Nadav Cohen, Shauli Shmilovich, Yaniv Oiknine, Adrian Stern

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    The utilization of compressive sensing (CS) techniques for hyperspectral (HS) imaging is appealing since HS data is typically huge and very redundant. The CS design offers a significant reduction of the acquisition effort, which can be manifested in faster acquisition of the HS datacubes, acquisition of larger HS images and removing the need for postacquisition digital compression. But, do all these benefits come at the expense of the ability to extract targets from the HS images? The answer to this question, of course, depends on the specific CS design and on the target detection algorithm employed. In a previous study we have shown that there is virtually no target detection performance degradation when a classical target detection algorithm is applied on data acquired with CS HS imaging techniques of the kind we have developed during the last years. In this paper we further investigate the robustness of our CS HS techniques for the task of object classification by deep learning methods. We show preliminary results demonstrating that deep neural network classifiers perform equally well when applied on HS data captured with our compressively sensed methods, as when applied on conventionally sensed HS data.

    Original languageAmerican English
    Title of host publicationAutomatic Target Recognition XXX
    EditorsRiad I. Hammoud, Timothy L. Overman, Abhijit Mahalanobis
    PublisherSPIE
    ISBN (Electronic)9781510635654
    DOIs
    StatePublished - 1 Jan 2020
    EventAutomatic Target Recognition XXX 2020 - Virtual, Online, United States
    Duration: 27 Apr 20208 May 2020

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    Volume11394

    Conference

    ConferenceAutomatic Target Recognition XXX 2020
    Country/TerritoryUnited States
    CityVirtual, Online
    Period27/04/208/05/20

    Keywords

    • Compressed sensing
    • Deep learning
    • Hyperspectral classification
    • Neural network.

    All Science Journal Classification (ASJC) codes

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering

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