Collaborative Representation for Hyperspectral Anomaly Detection
Beijing University of Chemical Technology · Mississippi State University
Abstract
In this paper, collaborative representation is proposed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. The representation is assumed to be the linear combination of neighboring pixels, and the collaboration of representation is reinforced by l 2 -norm minimization of the representation weight vector. To adjust the contribution of each neighboring pixel, a distance-weighted regularization matrix is included in the optimization problem, which has a simple and closed-form solution. By imposing the sum-to-one constraint to the weight vector, the…
Citation impact
- FWCI
- 28.89
- Percentile
- 100%
- References
- 45
Authors
2Topics & keywords
- Hyperspectral imaging
- Pixel
- Anomaly detection
- Pattern recognition (psychology)
- Artificial intelligence
- Computer science
- Representation (politics)
- Kernel (algebra)