Hyperspectral Anomaly Detection Using Attribute Profiles
- 23 May 2017
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 14 (7) , 1136-1140
Abstract
Over the past decade, anomaly detection has been an alluring topic in hyperspectral imagery. Despite most anomaly detection methods that focus on spectral information for detecting targets, this letter proposes a method in which both spectral and spatial information about the hyperspectral image (HSI) has been utilized for detecting anomalies. The differential attribute profile anomaly detection (DAPAD) method utilizes principal component analysis and DAP to extract spectral and spatial information from HSI, respectively. DAPs can model different kinds of structural information in a scene, which makes better extraction of spatial information. The proposed method is applied in experiments on Hyperspectral Digital Imagery Collection Experiment remote sensing data and the experimental results confirm the DAPAD method's superiority over five commonly used state-of-the-art anomaly detection methods.Keywords
This publication has 15 references indexed in Scilit:
- Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse RepresentationIEEE Transactions on Geoscience and Remote Sensing, 2015
- Hyperspectral Anomaly Detection by the Use of Background Joint Sparse RepresentationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
- Kernel Multivariate Spectral–Spatial Analysis of Hyperspectral DataIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
- Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral ImagesIEEE Geoscience and Remote Sensing Letters, 2015
- Feature Extraction Using Weighted Training SamplesIEEE Geoscience and Remote Sensing Letters, 2015
- An Adaptive Pixon Extraction Technique for Multispectral/Hyperspectral Image ClassificationIEEE Geoscience and Remote Sensing Letters, 2014
- A Survey on Spectral–Spatial Classification Techniques Based on Attribute ProfilesIEEE Transactions on Geoscience and Remote Sensing, 2014
- Collaborative Representation for Hyperspectral Anomaly DetectionIEEE Transactions on Geoscience and Remote Sensing, 2014
- Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral ImageryIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
- Hyperspectral Target Detection : An Overview of Current and Future ChallengesIEEE Signal Processing Magazine, 2013