Improved covariance matrices for point target detection in hyperspectral data-Reference-Cited by-同舟云学术

Improved covariance matrices for point target detection in hyperspectral data

Published:2008-07-01 Issue:7 Volume:47 Page:076402
ISSN:0091-3286
Container-title:Optical Engineering
language:en
Short-container-title:Opt. Eng

Author:

Silverman Jerry

Publisher

SPIE-Intl Soc Optical Eng

Subject

General Engineering,Atomic and Molecular Physics, and Optics

Reference27 articles.

1. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution

2. W. H. Farrand and J. C. Harsanyi , “Mapping distributed geological and botanical targets through constrained energy minimization,” inProc. Tenth Conf. on Geologic Remote Sensing, pp. 516–521 (1994).

3. Modeling hyperspectral imaging data

4. Statistical models for LWIR hyperspectral backgrounds and their applications in chemical agent detection

5. Real-time hyperspectral detection and cuing

Cited by 52 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Toward Efficient Hyperspectral Anomaly Detection With Subspace Transformation Learning;IEEE Geoscience and Remote Sensing Letters;2024

2. You Only Train Once: Learning a General Anomaly Enhancement Network With Random Masks for Hyperspectral Anomaly Detection;IEEE Transactions on Geoscience and Remote Sensing;2023

3. Data analysis using Riemannian geometry and applications to chemical engineering;Computers & Chemical Engineering;2022-12

4. Bayesian Non-Parametric Detector Based on the Replacement Model;IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium;2022-07-17

5. Estimating chemical concentrations from compressed hyperspectral images;Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII;2022-05-31