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).
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Wang, X., Luo, G. & Tian, L. Application of hyperspectral image anomaly detection algorithm for Internet of things. Multimed Tools Appl 78, 5155–5167 (2019). https://doi.org/10.1007/s11042-017-4682-1
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DOI: https://doi.org/10.1007/s11042-017-4682-1