WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF-Reference-Cited by-同舟云学术

WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF

Published:2020-12 Issue: Volume:250 Page:112012
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Zhong Yanfei^ORCID,Hu Xin,Luo Chang^ORCID,Wang Xinyu^ORCID,Zhao Ji^ORCID,Zhang Liangpei^ORCID

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference47 articles.

1. Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry;Adão;Remote Sens.,2017

2. Multiresolution segmentation:an optimization approach for high quality multi-scale image segmentation;Baatz;Angewandte Geographische Informationsverarbeitung,2000

3. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach;Cai;Remote Sens. Environ.,2018

4. LIBSVM: a library for support vector machines;Chang;ACM Trans. Intell. Syst. Technol.,2011

5. Deep feature extraction and classification of Hyperspectral images based on convolutional neural networks;Chen;IEEE Trans. Geosci. Remote Sens.,2016

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