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Hyperspectral anomaly detection via density peak clustering

  • Published:2020-01 Issue: Volume:129 Page:144-149
  • ISSN:0167-8655
  • Container-title:Pattern Recognition Letters
  • language:en
  • Short-container-title:Pattern Recognition Letters

Author:

Tu BingORCID,Yang Xianchang,Li Nanying,Zhou Chengle,He Danbing

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Hunan Provincial Innovation Foundation for Postgraduate

Scientific Research Program of Hunan Province Education Department

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference34 articles.

1. Hyperspectral image data analysis;Landgrebe;IEEE Signal Process Mag.,2002

2. Recent advances in techniques for hyperspectral image processing;Plaza;Remote Sens. Environ.,2009

3. Classification of hyperspectral remote sensing images with support vector machines;Melgani;IEEE Trans. Geosci. Remote Sens.,2004

4. A review of nonlinear hyperspectral unmixing methods;Heylen;IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.,2014

5. Detection algorithms for hyperspectral imaging applications.;Manolakis;IEEE Signal Process. Mag.,2002

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