Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data

By J. Molero, E. M. Garzón, I. García, Antonio J. Plaza. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013

295 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/8d5e5b680c3206d85832203ebcbb0f8295b7bfe5

How researchers see this paper

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

In Artificial Intelligence

1. Lina Zhuang
2. Antonio J. Plaza
3. Degang Wang
4. Xu Sun
5. Min Huang
6. Lianru Gao

In Atmospheric Science

1. Antonio J. Plaza
2. Lianru Gao
3. Lina Zhuang
4. José M. Bioucas-Dias
5. Chein-I. Chang

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): “For example, the local RX (LRX) [10] method constructs statistics only from the surrounding pixels of the testing pixel (instead of the global pixels).”