Improving wetland cover classification using artificial neural networks with ensemble techniques-Reference-Cited by-同舟云学术

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Improving wetland cover classification using artificial neural networks with ensemble techniques

Published:2021-05-19 Issue:4 Volume:58 Page:603-623
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Hu Xudong¹,Zhang Penglin¹²,Zhang Qi¹,Wang Junqiang³⁴

Affiliation:

1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

2. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, China

3. Division of Pressure Pipelines, China Special Equipment Inspection and Research Institute, Beijing, China

4. Technology Innovation center of Oil and Gas Pipeline and Storage Equipment Safety for State Market Regulation, Beijing, China

Funder

National Key Research and Development Program of China

Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2021.1932126

Reference86 articles.

1. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

2. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

3. A Multiple Classifier System to improve mapping complex land covers: a case study of wetland classification using SAR data in Newfoundland, Canada

4. Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

5. Artificial neural networks: fundamentals, computing, design, and application

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