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International Journal of Remote Sensing Volume 17, 1996 - Issue 3
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Original Articles

Sparse inverse covariance matrices and efficient maximum likelihood classification of hyperspectral data

R. E. ROGER Department of Electrical Engineering, University College University of New South Wales, Australian Defence Force Academy, Canberra, ACT2600, Australia
Pages 589-613 | Received 25 Oct 1994, Accepted 12 Jun 1995, Published online: 27 Apr 2007

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Read on this site (2)

AnthonyM. Filippi, Christian Brannstrom, Iliyana Dobreva, DavidM. Cairns & Daehyun Kim. (2009) Unsupervised Fuzzy ARTMAP Classification of Hyperspectral Hyperion Data for Savanna and Agriculture Discrimination in the Brazilian Cerrado. GIScience & Remote Sensing 46:1, pages 1-23.
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R. E. ROGER. (1996) Principal Components transform with simple, automatic noise adjustment. International Journal of Remote Sensing 17:14, pages 2719-2727.
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Articles from other publishers (12)

Ying Zhan, Yufeng Wang & Xianchuan Yu. (2023) Semisupervised hyperspectral image classification based on generative adversarial networks and spectral angle distance. Scientific Reports 13:1.
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Francis J. Sousa & Daniel J. Sousa. (2022) Hyperspectral Reconnaissance: Joint Characterization of the Spectral Mixture Residual Delineates Geologic Unit Boundaries in the White Mountains, CA. Remote Sensing 14:19, pages 4914.
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John A. Richards & Nick G. Kingsbury. (2014) Is There a Preferred Classifier for Operational Thematic Mapping?. IEEE Transactions on Geoscience and Remote Sensing 52:5, pages 2715-2725.
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Are C. Jensen, AsbjØrn Berge & Anne Schistad Solberg. (2008) Regression Approaches to Small Sample Inverse Covariance Matrix Estimation for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 46:10, pages 2814-2822.
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Are C. Jensen, Asbjorn Berge & Anne Schistad Solberg. (2007) Regression approaches to small sample inverse covariance matrix estimation for hyperspectral image classification. Regression approaches to small sample inverse covariance matrix estimation for hyperspectral image classification.
Anthony M. Filippi & John R. Jensen. (2006) Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing of Environment 100:4, pages 512-530.
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JongGyu Han, KwangHoon Chi & YeonKwang Yeon. 2005. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing 251 262 .
Jong-Gyu Han, Keun-Ho Ryu, -Kwang Yeon & Kwang-Hoon Chi. (2002) Land Surface Classification With Airborne Multi-spectral Scanner Image Using A Neuro-Fuzzy Model. The KIPS Transactions:PartD 9D:5, pages 939-944.
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N.T. Santich. (2002) Reducing the parameterization of the covariance matrix for maximum likelihood classification. Reducing the parameterization of the covariance matrix for maximum likelihood classification.
Fuan Tsai & W.D. Philpot. (2002) A derivative-aided hyperspectral image analysis system for land-cover classification. IEEE Transactions on Geoscience and Remote Sensing 40:2, pages 416-425.
Crossref
J.A. Bilmes. (2000) Factored sparse inverse covariance matrices. Factored sparse inverse covariance matrices.
Sang Gu Lee, Jong Gru Han, Kwang Hoon Chi, Jae Young Suh, Hee Hyol Lee, M. Miyazaki & K. Akizuki. (1999) A neuro-fuzzy classifier for land cover classification. A neuro-fuzzy classifier for land cover classification.

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