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Hyperspectral Target Detection

  • Chein-I Chang
Chapter

Abstract

Target detection can be performed either in a supervised or an unsupervised manner, depending on the level of target knowledge being used for detection. On the other hand, it can also be performed either at full pixel level or subpixel scale, depending on whether a target is smaller than pixel resolution. This chapter takes a rather different point of view to look into target detection according to its applications, either in active or passive mode. Active target detection requires a certain level of target knowledge being used to find specific targets of interest. Examples include reconnaissance in military applications, such as finding potential targets of interest via U-2 reconnaissance planes, Unmanned Aerial Vehicles (UAVs) or Unmanned Aerial Systems (UAS), drones such as predator for air strikes, and search and rescue applications such as looking for missing objects and targets using Light Detection and Ranging (Lidar) (also known as LIDAR or LiDAR). Passive target detection assumes no prior knowledge at all, and is carried out in a completely unknown environment with no specific targets of interest to be sought. Examples include surveillance in military applications such as monitoring unusual and abnormal activities over regions of potential threats by Airborne Warning And Control System (AWACS) surveillance airplanes and searching for anomalies in agriculture applications by Forward looking infrared (FLIR) sensors.

Keywords

Target Detection Anomaly Detector Background Suppression Spectral Angle Mapper Pixel Purity Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2016

Authors and Affiliations

  1. 1.BaltimoreUSA

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