Machine learning-based temporal mixture analysis of hypertemporal Antarctic sea ice data-Reference-Cited by-同舟云学术

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Machine learning-based temporal mixture analysis of hypertemporal Antarctic sea ice data

Published:2015-12-03 Issue:2 Volume:7 Page:190-199
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Chi Junhwa,Kim Hyun-Cheol,Kang Sung-Ho

Funder

KOPRI-Satellite remote sensing on west Antarctic Ocean research

The Ministry of Ocean and Fishery

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

http://www.tandfonline.com/doi/pdf/10.1080/2150704X.2015.1121300

Reference16 articles.

1. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

2. Spectral Unmixing-Based Crop Residue Estimation Using Hyperspectral Remote Sensing Data: A Case Study at Purdue University

3. Global continuous fields of vegetation characteristics: A linear mixture model applied to multi-year 8 km AVHRR data

4. A transformation for ordering multispectral data in terms of image quality with implications for noise removal

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3. The Spectral Mixture Residual: A Source of Low‐Variance Information to Enhance the Explainability and Accuracy of Surface Biology and Geology Retrievals;Journal of Geophysical Research: Biogeosciences;2022-02

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