Lithological Classification Using Densely Connected Convolution Network on Landsat-9 and Aster Datasets in a Semi-Arid Environment-Reference-Cited by-同舟云学术

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Lithological Classification Using Densely Connected Convolution Network on Landsat-9 and Aster Datasets in a Semi-Arid Environment

Published:2023-12-15 Issue: Volume: Page:1-5
ISSN:
Container-title:2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)
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Author:

Appiah-Twum Michael¹,Wenbo Xu¹,Mawuli Cobbinah B.²,Atandoh Peter³

Affiliation:

1. School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,Sichuan,China,611731

2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China,Huzhou,China,313001

3. School of Information and Software Engineering, University of Electronic Science and Technology of China,Chengdu,Sichuan,China,611731

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10386992/10385244/10387076.pdf?arnumber=10387076

Reference11 articles.

1. Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information

2. Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

3. Geological Remote Sensing Interpretation Using Deep Learning Feature and an Adaptive Multisource Data Fusion Network

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