Next Article in Journal
A Mineralized Alga and Acritarch Dominated Microbiota from the Tully Formation (Givetian) of Pennsylvania, USA
Next Article in Special Issue
A Special Issue of Geosciences: Mapping and Assessing Natural Disasters Using Geospatial Technologies
Previous Article in Journal
Network Modelling of the Influence of Swelling on the Transport Behaviour of Bentonite
Previous Article in Special Issue
Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography
Article Menu

Export Article

Open AccessArticle
Geosciences 2016, 6(4), 56; doi:10.3390/geosciences6040056

Anomaly Detection from Hyperspectral Remote Sensing Imagery

School of Geosciences, University of South Florida, Tampa, FL 33620, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Yongwei Sheng and Jesus Martinez-Frias
Received: 18 May 2016 / Revised: 30 October 2016 / Accepted: 2 December 2016 / Published: 12 December 2016
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
View Full-Text   |   Download PDF [6434 KB, uploaded 12 December 2016]   |  

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/Infrared Imaging Spectrometer (AVIRIS) data covering the post-attack World Trade Center (WTC) and anomalies are fire spots. The other data set called SpecTIR contained fabric panels as anomalies compared to their background. Existing anomaly detection algorithms including the Reed–Xiaoli detector (RXD), the blocked adaptive computation efficient outlier nominator (BACON), the random selection based anomaly detector (RSAD), the weighted-RXD (W-RXD), and the probabilistic anomaly detector (PAD) are reviewed here. The RXD generally sets strict assumptions to the background, which cannot be met in many scenarios, while BACON, RSAD, and W-RXD employ strategies to optimize the estimation of background information. The PAD firstly estimates both background information and anomaly information and then uses the information to conduct anomaly detection. Here, the BACON, RSAD, W-RXD, and PAD outperformed the RXD in terms of detection accuracy, and W-RXD and PAD required less time than BACON and RSAD. View Full-Text
Keywords: hyperspectral imagery; anomaly detection; fire mapping hyperspectral imagery; anomaly detection; fire mapping
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Guo, Q.; Pu, R.; Cheng, J. Anomaly Detection from Hyperspectral Remote Sensing Imagery. Geosciences 2016, 6, 56.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top