A machine learning approach to hyperspectral detection of solid targets-Reference-Cited by-同舟云学术

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A machine learning approach to hyperspectral detection of solid targets

Published:2018-05-08 Issue: Volume: Page:
ISSN:
Container-title:Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
language:
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Author:

Ziemann Amanda K.,Theiler James,Kucer Michal

Publisher

SPIE

Reference33 articles.

1. Rapid Convergence Rate in Adaptive Arrays

2. Performance of an adaptive detection algorithm; rejection of unwanted signals

3. A CFAR adaptive matched filter detector

4. Adaptive matched subspace detectors and adaptive coherence estimators;Scharf,1996

5. The adaptive coherence estimator: a uniformly most-powerful-invariant adaptive detection statistic

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2. Bayesian vs Generalized Likelihood Ratio Detection of Solid Sub-Pixel Targets;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

3. Cfarnet: Deep Learning for Target Detection with Constant False Alarm Rate;2023

4. Iterative R&R (rotation and remarginalization) for detecting targets in spectral imagery;Imaging Spectrometry XXV: Applications, Sensors, and Processing;2022-09-30

5. Learning to Detect with Constant False Alarm Rate;2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC);2022-07-04

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