River Channel Microgeomorphic Feature Extraction and Potential Sandstorm Source Identification Method Based on a Convolutional Autoencoder Model
River channel's microgeomorphic features are crucial for identifying potential sandstorm sources and studying sediment source-sink processes. Current deep learning methods are predominantly applied to visible objects, rendering them unsuitable for latent objects with unstable spatiotempor...
Saved in:
Main Authors: | Kecong Wu, Lirong Chen, Yalige Bai, Xinhang Wang, Danzeng Pingcuo, Zhongpeng Han, Chengshan Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10812012/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Characterizing Changes in Geometry and Flow Speeds of Land- and Lake-Terminating Glaciers at the Headwaters of Yarlung Zangbo River, Western Himalayas
by: Min Zhou, et al.
Published: (2024-12-01) -
Impacts of Sandstorms on Chemistries of Ambient PAHs in a Small City in North China
by: Zhiyong Li, et al.
Published: (2023-03-01) -
A convolutional autoencoder framework for ECG signal analysis
by: Ugo Lomoio, et al.
Published: (2025-01-01) -
Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis
by: Tomasz Walczyna, et al.
Published: (2024-12-01) -
Modified Autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning
by: Nicholas Merrill, et al.
Published: (2020-01-01)