Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X-Reference-Cited by-同舟云学术

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Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X

Published:2014-11-26 Issue:23 Volume:35 Page:7898-7909
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Sonobe Rei¹,Tani Hiroshi²,Wang Xiufeng²,Kobayashi Nobuyuki³,Shimamura Hideki⁴

Affiliation:

1. JSPS Research Fellow/Graduate School of Agriculture, Hokkaido University, Sapporo, Hokkaido 060-8589, Japan

2. Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido 060-8589, Japan

3. Smart Link Hokkaido, Iwamizawa, Hokkaido 068-0034, Japan

4. PASCO Corporation, Meguro-Ku, Tokyo 153-0043, Japan

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2014.978038

Reference36 articles.

1. Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data

2. Multiyear independent validation of the water cloud model for retrieving maize leaf area index from SAR time series

3. Retrieving crop parameters based on tandem ERS 1/2 interferometric coherence images

4. C-band polarimetric indexes for maize monitoring based on a validated radiative transfer model

5. A Novel Technique for Subpixel Image Classification Based on Support Vector Machine

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