Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows
Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summati...
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Online Access: | http://www.mdpi.com/2072-4292/10/1/103 |
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doaj-8ba1feb3dce74f5c9ab5a5e69860de3c2020-11-24T22:15:57ZengMDPI AGRemote Sensing2072-42922018-01-0110110310.3390/rs10010103rs10010103Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding WindowsLiaoying Zhao0Weijun Lin1Yulei Wang2Xiaorun Li3Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaInstitute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaCenter for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian 116026, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaAnomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the W o o d b u r y identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors.http://www.mdpi.com/2072-4292/10/1/103hyperspectral imageryrecursive anomaly detectionlocal summation RX detector (LS-RXD)sliding window |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liaoying Zhao Weijun Lin Yulei Wang Xiaorun Li |
spellingShingle |
Liaoying Zhao Weijun Lin Yulei Wang Xiaorun Li Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows Remote Sensing hyperspectral imagery recursive anomaly detection local summation RX detector (LS-RXD) sliding window |
author_facet |
Liaoying Zhao Weijun Lin Yulei Wang Xiaorun Li |
author_sort |
Liaoying Zhao |
title |
Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows |
title_short |
Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows |
title_full |
Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows |
title_fullStr |
Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows |
title_full_unstemmed |
Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows |
title_sort |
recursive local summation of rx detection for hyperspectral image using sliding windows |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2018-01-01 |
description |
Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the
W
o
o
d
b
u
r
y
identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors. |
topic |
hyperspectral imagery recursive anomaly detection local summation RX detector (LS-RXD) sliding window |
url |
http://www.mdpi.com/2072-4292/10/1/103 |
work_keys_str_mv |
AT liaoyingzhao recursivelocalsummationofrxdetectionforhyperspectralimageusingslidingwindows AT weijunlin recursivelocalsummationofrxdetectionforhyperspectralimageusingslidingwindows AT yuleiwang recursivelocalsummationofrxdetectionforhyperspectralimageusingslidingwindows AT xiaorunli recursivelocalsummationofrxdetectionforhyperspectralimageusingslidingwindows |
_version_ |
1725791999814008832 |