Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow-Reference-Cited by-同舟云学术

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

Published:2017-07-11 Issue:7 Volume:11 Page:670-690
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Friedel Michael J.¹²,Buscema Massimo²³,Vicente Luiz Eduardo⁴,Iwashita Fabio⁵,Koga-Vicente Andréa⁶

Affiliation:

1. Hydrogeology Department, GNS Science, Lower Hutt, New Zealand

2. Mathematical & Statistical Sciences, University of Colorado, Denver, CO, USA

3. Semeion Institute, Rome, Italy

4. Satellite Monitoring Unit, Brazilian Corporation of Agricultural Research-EMBRAPA, Campinas, Brazil

5. Earth Sciences Department, University of Florence, Florence, Italy

6. Center for Meteorological and Climatological Research in Agriculture, University of Campinas, Campinas, Brazil

Funder

Ministry of Business, Innovation and Employment

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2017.1349841

Reference46 articles.

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3. A new geometric model for clustering high-resolution satellite images

4. The ASTER spectral library version 2.0

5. Barry, P. 2001. EO-1/ Hyperion Science Data User’s Guide. Level 1_B. Redondo Beach: TRW Space, Defense & Information Systems, 60 pp.

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