A new method for the extraction of tailing ponds from very high-resolution remotely sensed images: PSVED-Reference-Cited by-同舟云学术

A new method for the extraction of tailing ponds from very high-resolution remotely sensed images: PSVED

Published:2023-07-11 Issue:1 Volume:16 Page:2681-2703
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Zhang Chengye¹,Xing Jianghe¹,Li Jun¹,Du Shouhang¹²,Qin Qiming³

Affiliation:

1. College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, People’s Republic of China

2. Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing, People’s Republic of China

3. Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

National Key Research and Development Program

Fundamental Research Funds for the Central Universities

Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2234338

Reference57 articles.

1. MsanlfNet: Semantic Segmentation Network With Multiscale Attention and Nonlocal Filters for High-Resolution Remote Sensing Images

2. Deep Feature Fusion for VHR Remote Sensing Scene Classification

3. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

4. Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster

5. Xception: Deep Learning with Depthwise Separable Convolutions

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