Joint subspace detection of hyperspectral targets

By Alan P. Schaum. IEEE Aerospace Conference. Proceedings, 2004

113 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/3ad4667ea2c7f0fb67b7c1afad7dd9d26517eb26

How researchers see this paper

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

In Aerospace Engineering

1. Gustau Camps-Valls
2. José M. Bioucas-Dias
3. Antonio J. Plaza
4. Bo Du
5. Paul Scheunders
6. Jocelyn Chanussot
7. Nasser M. Nasrabadi
8. Liangpei Zhang

In Media Technology

1. Antonio J. Plaza
2. Jocelyn Chanussot
3. José M. Bioucas-Dias
4. Liangpei Zhang
5. Shutao Li
6. Bo Du
7. Jon Atli Benediktsson
8. Nasser M. Nasrabadi

Citations in the corpus

• A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection (2016, background): “Although these approaches can produce accurate estimation of the background, they cannot easily distinguish between anomalies and noise [13], [14].”
• Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “In [146], a modification to the RX algorithm called SubSpace RX (SSRX) was outlined that is based on the PCA of the background covariance matrix.”