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Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5155–5167 | Cite as

Application of hyperspectral image anomaly detection algorithm for Internet of things

  • Xinjian WangEmail author
  • Guangchun Luo
  • Ling Tian
Article
  • 182 Downloads

Abstract

Hyperspectral image(HSI) anomaly detection, as one of the hottest topics in current remote sensing information processing and image processing,has important theoretical value and has been widely used in military and civilian applications. Anomaly detection aims to detect and label small man-made abnormal targets or objects without any prior knowledge. In this paper, we proposed a segmented three-order Tucker decomposition for HSI anomaly detection. There are three major steps:1) the original HSI data is divided along the three dimensions into a grid of multiple of small-sized sub-tensors. 2)Tucker decomposition followed by anomaly detection algorithm is applied onto each sub-tensor. 3) the detection results from those sub-tensors are fused. Experiments reveal that the proposed method outperforms other current anomaly detectors with better detection performance. Finally, we introduce the application of hyperspectral image anomaly detection algorithm in the Internet of things(IOT).

Keywords

Hyperspectral imagery (HSI) Anomaly detection Segmented three-order Tucker decomposition Internet of things(IOT) 

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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