Automated Hyperspectral Cueing for Civilian Search and Rescue

被引:190
作者
Eismann, Michael T. [1 ]
Stocker, Alan D. [2 ]
Nasrabadi, Nasser M. [3 ]
机构
[1] USAF, Res Lab, Wright Patterson AFB, OH 45433 USA
[2] Space Comp Corp, Los Angeles, CA 90025 USA
[3] USA, Res Lab, Adelphi, MD 20783 USA
关键词
Change detection; hyperspectral; remote sensing; search-and-rescue; target detection; ORTHOGONAL SUBSPACE PROJECTION; ANOMALY DETECTION; MATCHED-FILTER; ATMOSPHERIC CORRECTION; IMAGING SPECTROMETER; OBJECT DETECTION; TARGET DETECTION; LAND-COVER; CLASSIFICATION; HYPERION;
D O I
10.1109/JPROC.2009.2013561
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Hyperspectral remote sensing provides information related to surface material characteristics that can be exploited to perform automated detection of targets of interest and has been applied to a variety of remote sensing applications. This paper explores the application to civilian search and rescue, using the Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) system developed for the Civil Air Patrol as a key example of how evolving hyperspectral technology can be employed to support these operations. ARCHER combines a visible/near-infrared hyperspectral imaging system, a high-resolution visible panchromatic imaging sensor, and an integrated geopositioning and inertial navigation unit with onboard real-time processing for data acquisition and correction, precision image georegistration, and target detection and cueing. Processing for detecting downed aircraft wreckage and other related objects employs real-time adaptive anomaly detection and matched filtering algorithms, and a non-real-time change detection mode to provide further false alarm reduction in some instances. This paper describes the system technology, with an emphasis on the current and evolving automated target detection methods, and summarizes the operational experience in the airborne employment against civilian search and rescue missions.
引用
收藏
页码:1031 / 1055
页数:25
相关论文
共 103 条
  • [81] Joint hyperspectral subspace detection derived from a Bayesian Likelihood Ratio test
    Schaum, A
    Stocker, A
    [J]. ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY VIII, 2002, 4725 : 225 - 233
  • [82] Subclutter target detection using sequences of thermal infrared multispectral imagery
    Schaum, A
    Stocker, A
    [J]. ALGORITHMS FOR MULTISPECTRAL AND HYPERSPECTRAL IMAGERY III, 1997, 3071 : 12 - 22
  • [83] Schaum A., 1997, PROC INT S SPECTRAL
  • [84] SCHAUM A, 1997, P ISSR
  • [85] Scholkopf Bernhard, 2002, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
  • [86] Hyperspectral imagery: Clutter adaptation in anomaly detection
    Schweizer, SM
    Moura, JMF
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2000, 46 (05) : 1855 - 1871
  • [87] COMPACT Airborne Spectral Sensor (COMPASS)
    Simi, C
    Winter, E
    Williams, M
    Driscoll, D
    [J]. ALGORITHMS FOR MULTISPECTRAL, HYPERSPECTRAL AND ULTRASPECTRAL IMAGERY VII, 2001, 4381 : 129 - 136
  • [89] SMETEK TE, 2007, P 2007 IEEE AER C BI
  • [90] STEIN D, 2003, P IEEE WORKSH ADV TE, P44