A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection

By Yuxiang Zhang, Bo Du, Liangpei Zhang, Shugen Wang. IEEE Transactions on Geoscience and Remote Sensing, 2016

434 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/4c9e2d100dd2104a916bdd979cb42527320d09e8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Antonio J. Plaza
2. Lina Zhuang
3. José M. Bioucas-Dias

In Computer Vision and Pattern Recognition

1. Antonio J. Plaza
2. Lianru Gao
3. Lina Zhuang

In Media Technology

1. Lina Zhuang
2. Lianru Gao
3. Antonio J. Plaza
4. Degang Wang
5. Xu Sun
6. Min Huang
7. José M. Bioucas-Dias
8. Jocelyn Chanussot

Citations in the corpus

• BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection (2023, methodology): “Zhang et al. [36] utilized the Mahalanobis distance between each testing pixel and the extracted low-rank matrix for HAD.”