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Fast real-time onboard processing of hyperspectral imagery for detection and classification

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Abstract

Remotely sensed hyperspectral imagery has many important applications since its high-spectral resolution enables more accurate object detection and classification. To support immediate decision-making in critical circumstances, real-time onboard implementation is greatly desired. This paper investigates real-time implementation of several popular detection and classification algorithms for image data with different formats. An effective approach to speeding up real-time implementation is proposed by using a small portion of pixels in the evaluation of data statistics. An empirical rule of an appropriate percentage of pixels to be used is investigated, which results in reduced computational complexity and simplified hardware implementation. An overall system architecture is also provided.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

    Qian Du

  2. Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX, USA

    Reza Nekovei

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  1. Qian Du
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  2. Reza Nekovei
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Correspondence to Qian Du.

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Du, Q., Nekovei, R. Fast real-time onboard processing of hyperspectral imagery for detection and classification. J Real-Time Image Proc 4, 273–286 (2009). https://doi.org/10.1007/s11554-008-0106-9

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  • DOI: https://doi.org/10.1007/s11554-008-0106-9

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