Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data

Approved for public release; distribution is unlimited === Due to the United States dependency on maritime travel, the proliferation of efficient and inexpensive naval mines poses a tremendous risk. Current MCM technologies have narrow FOVs, preventing timely, wide-area searches. These technologies...

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Main Author: Burt, Christopher B.
Other Authors: Olsen, Richard C.
Published: Monterey, California. Naval Postgraduate School 2012
Online Access:http://hdl.handle.net/10945/17330
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-173302015-08-06T16:03:02Z Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data Burt, Christopher B. Olsen, Richard C. Trask, David Remote Sensing Intelligence Approved for public release; distribution is unlimited Due to the United States dependency on maritime travel, the proliferation of efficient and inexpensive naval mines poses a tremendous risk. Current MCM technologies have narrow FOVs, preventing timely, wide-area searches. These technologies require the operator to be in proximity to the targets, a dangerous scenario made worse when in denied territory. In an effort to mitigate these risks, the use of a high altitude hyperspectral sensor is proposed. The operational ability of a hyperspectral sensor to detect sub-pixel surface and submerged mines in non-littoral environments was evaluated using visual inspection and two common anomaly detectors Mixture Tuned Matched Filtering (MTMF) and Reed-Xiaoli (RX). Due to the unavailability of the DoDs Spectral Infrared Imaging Technology Testbed (SPIRITT), ProSpecTIR-VS3, a sensor similar spatially and spectrally to SPIRITT was flown over a range offshore California. This experiment included three surface and three submerged targets, each with a 0.8 meter diameter. Both 0.5 and 1 meter spatial resolution data were collected, allowing for both a resolved and unresolved analysis. While both anomaly detection techniques have their flaws, the success of this study is in proving the usefulness of hyperspectral data for sub-pixel mine detection. 2012-11-14T00:02:16Z 2012-11-14T00:02:16Z 2012-09 Thesis http://hdl.handle.net/10945/17330 Monterey, California. Naval Postgraduate School
collection NDLTD
sources NDLTD
description Approved for public release; distribution is unlimited === Due to the United States dependency on maritime travel, the proliferation of efficient and inexpensive naval mines poses a tremendous risk. Current MCM technologies have narrow FOVs, preventing timely, wide-area searches. These technologies require the operator to be in proximity to the targets, a dangerous scenario made worse when in denied territory. In an effort to mitigate these risks, the use of a high altitude hyperspectral sensor is proposed. The operational ability of a hyperspectral sensor to detect sub-pixel surface and submerged mines in non-littoral environments was evaluated using visual inspection and two common anomaly detectors Mixture Tuned Matched Filtering (MTMF) and Reed-Xiaoli (RX). Due to the unavailability of the DoDs Spectral Infrared Imaging Technology Testbed (SPIRITT), ProSpecTIR-VS3, a sensor similar spatially and spectrally to SPIRITT was flown over a range offshore California. This experiment included three surface and three submerged targets, each with a 0.8 meter diameter. Both 0.5 and 1 meter spatial resolution data were collected, allowing for both a resolved and unresolved analysis. While both anomaly detection techniques have their flaws, the success of this study is in proving the usefulness of hyperspectral data for sub-pixel mine detection.
author2 Olsen, Richard C.
author_facet Olsen, Richard C.
Burt, Christopher B.
author Burt, Christopher B.
spellingShingle Burt, Christopher B.
Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data
author_sort Burt, Christopher B.
title Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data
title_short Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data
title_full Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data
title_fullStr Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data
title_full_unstemmed Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data
title_sort detection of spatially unresolved (nominally sub-pixel) submerged and surface targets using hyperspectral data
publisher Monterey, California. Naval Postgraduate School
publishDate 2012
url http://hdl.handle.net/10945/17330
work_keys_str_mv AT burtchristopherb detectionofspatiallyunresolvednominallysubpixelsubmergedandsurfacetargetsusinghyperspectraldata
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