Towards interpreting multi-temporal deep learning models in crop mapping-Reference-Cited by-同舟云学术

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Towards interpreting multi-temporal deep learning models in crop mapping

Published:2021-10 Issue: Volume:264 Page:112599
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Xu Jinfan,Yang Jie,Xiong Xingguo,Li Haifeng,Huang Jingfeng,Ting K.C.,Ying Yibin,Lin Tao

Funder

Zhejiang University

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference77 articles.

1. Landsat-based classification in the cloud: an opportunity for a paradigm shift in land cover monitoring;Azzari;Remote Sens. Environ.,2017

2. Random forest in remote sensing: a review of applications and future directions;Belgiu;ISPRS J. Photogramm. Remote Sens.,2016

3. Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program;Boryan;Geocarto Int.,2011

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