19 May 2016 Real-time hyperspectral anomaly detection via band-interleaved by line
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Abstract
Anomaly detection is one of most fundamental tasks in hyperspectral data exploitation. Since anomalies are generally unknown and unexpected, their detection must be carried out without prior knowledge. Most importantly, when anomalies are weak and moving as time goes on real time processing of anomaly detection becomes immense in detecting these anomalous targets. Due to hyperspectral sensor design two major formats are used for data acquisition in real-time. One is real-time sample processing which collects data in two different fashions, Band-interleaved by Pixel/Sample (BIP/BIS) sample-by-sample and Band-Interleaved-by-Line (BIL) line-by-line. Another is real-time band processing which follows the Band Sequential (BSQ) format to collect the data. Recently, anomaly detection using both BIP/BIS and BSQ has been reported in the literature. Since a hyperspectral imaging sensor generally collects the data in a push-broom manner, the BIL format is preferred to BIP/BIS. But it is interesting to note that AD using BIL has not been explored and investigated in the past mainly because it was expected that AD using BIL may perform similarly to AD using BIP/BIS. This paper shows otherwise due to the fact that the covariance/correlation matrix used by an anomaly detector has significant impact on the detectability of anomalies. It has been shown that anomaly detection is heavily determined by the ratio of anomalies to be detected to the image size that forms the covariance/correlation matrix. So, when AD using BIL is implemented, the information of covariance/correlation matrix provided BIL is different from that provided by BIP/BIS. As a result, it is anticipated that AD using BIL may result different performance from AD using BIP/BIS.
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Hsiao-Chi Li, Chein-I Chang, "Real-time hyperspectral anomaly detection via band-interleaved by line", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740H (19 May 2016); doi: 10.1117/12.2228246; https://doi.org/10.1117/12.2228246
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