Object-Oriented and Deep-Learning-Based High-Resolution Mapping from Large Remote Sensing Imagery-Reference-Cited by-同舟云学术

Object-Oriented and Deep-Learning-Based High-Resolution Mapping from Large Remote Sensing Imagery

Published:2021-05-04 Issue:3 Volume:47 Page:396-412
ISSN:0703-8992
Container-title:Canadian Journal of Remote Sensing
language:en
Short-container-title:Canadian Journal of Remote Sensing

Author:

Wu Yijin¹,Zhang Pengfei¹,Wu Jing¹,Li Chang¹

Affiliation:

1. Key Laboratory for Geographical Process Analysis and Simulation, College of Urban and Environmental Science, Central China Normal University, Hubei Province, China

Funder

National Natural Science Foundation of China

Wuhan Youth Science and Technology Plan

National Key Research and Development Program

Hubei Soil and Water Conservation Monitoring Research

self-determined research funds of CCNU from the basic research and operation of MOE

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/07038992.2021.1944802

Reference43 articles.

1. Object-Based Classification of Greenhouses Using Sentinel-2 MSI and SPOT-7 Images: A Case Study from Anamur (Mersin), Turkey

2. A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates

3. Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification

4. Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

5. A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

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