Translator Disclaimer
Paper
19 February 2013 Parallel algorithms for fast subpixel detection in hyperspectral imagery
Author Affiliations +
Proceedings Volume 8655, Image Processing: Algorithms and Systems XI; 86550O (2013) https://doi.org/10.1117/12.2001537
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States
Abstract
We present parallel algorithms for fast subpixel detection of targets in hyperspectral imagery produced by our Hyperspectral Airborne Tactical Instrument (HATI-2500). The HATI-2500 hyperspectral imaging system has a blue-enhanced visible-near-IR (VNIR) and a full short-wave IR (SWIR) range response from 400 to 2500 nm. It has an industry-leading spectral resolution that ranges from 6 nm down to 1.5 nm in the VNIR region. The parallel detection algorithm selected for processing the hyperspectral data cubes is based on the adaptive coherence/cosine estimator (ACE). The ACE detector is a robust detector that is built upon the theory of generalized likelihood ratio testing (GLRT) in implementing the matched subspace detector to unknown parameters such as the noise covariance matrix. Subspace detectors involve projection transformations whose matrices can be efficiently manipulated through multithreaded massively parallel processors on modern graphics processing units (GPU). The GPU kernels developed in this work are based on the CUDA computing architecture. We constrain the detection problem to a model with known target spectral features and unstructured background. The processing includes the following steps: 1) scale and offset applied to convert the data from digital numbers to radiance values, 2) update the background inverse covariance estimate in a line-by-line manner, and 3) apply the ACE detector for each pixel for binary hypothesis testing. As expected, the algorithm is extremely effective for homogeneous background, such as open desert areas; and less effective in mixed spectral regions, such as those over urban areas. The processing rate is shown to be faster than the maximum frame rate of the camera (100 Hz) with a comfortable margin.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chung M. Wong, John Shepanski, and Stephanie Sandor-Leahy "Parallel algorithms for fast subpixel detection in hyperspectral imagery", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550O (19 February 2013); https://doi.org/10.1117/12.2001537
PROCEEDINGS
12 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Target detection performance of hyperspectral imagers
Proceedings of SPIE (October 17 2019)
Real-time airborne hyperspectral detection systems
Proceedings of SPIE (August 23 2000)
Adaptive hyperspectral band selection
Proceedings of SPIE (September 10 2005)

Back to Top