Anomaly Detection from Hyperspectral Remote Sensing Imagery

Abstract

Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly detection. One data set was an Airborne Visible… (More)

Topics

Statistics

0204020142015201620172018
Citations per Year

149 Citations

Semantic Scholar estimates that this publication has 149 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Guo2016AnomalyDF, title={Anomaly Detection from Hyperspectral Remote Sensing Imagery}, author={Qiandong Guo and Ruiliang Pu and Jun Cheng}, year={2016} }