An Unsupervised Deep Hyperspectral Anomaly Detector

Sensors
Ning MaPhilip H W Leong

Abstract

Hyperspectral image (HSI) based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN) based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD), local RX detector (LRXD) and the-state-of-t...Continue Reading

References

Jun 13, 2006·Neural Computation·Geoffrey E HintonYee-Whye Teh
Jun 22, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yoshua BengioPascal Vincent
Dec 3, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Jürgen Schmidhuber

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Citations

Sep 1, 2019·Neural Networks : the Official Journal of the International Neural Network Society·Weiying XieXiuping Jia
May 8, 2019·Journal of Imaging·Alberto SignoroniSergio Benini
Dec 18, 2020··Sertac ArisoyKoray Kayabol

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Software Mentioned

AD
RXD
Matlab
DBN
Image Code

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