Maximized subspace model for hyperspectral anomaly detection

Author:

Lo Edisanter

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition

Reference23 articles.

1. Schaum AP (2007) Hyperspectral anomaly detection beyond RX. In: Proceeding of 13th SPIE conference on algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery, vol 6565, p 656502

2. Stein DWJ, Beaven SG, Hoff LE, Winter EM, Schaum AP, Stocker AD (2002) Anomaly detection from hyperspectral imagery. IEEE Signal Process Mag 19:58–69

3. Schaum AP (2006) Hyperspectral detection algorithms: from old ideas to operational concepts to next generation. In: Proceeding of 12th SPIE conference on algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery, vol 6233, p 623305

4. Horwitz HM, Nalepka RF, Hyde PD, Morgenstern JP (1971) Estimating the proportions of objects within a single resolution element of a multispectral sensor. In: Proceeding of 7th international symposium on remote sensing of environment (Ann Arbor, MI), pp 1307–1320

5. Stocker A, Schaum A (1997) Application of stochastic mixing models to hyperspectral detection problems. In: Proceeding of 3rd SPIE conference on algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery, vol 3071, pp 47–60

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