From Trained to Untrained: A Novel Change Detection Framework Using Randomly Initialized Models With Spatial–Channel Augmentation for Hyperspectral Images-Reference-Cited by-同舟云学术

From Trained to Untrained: A Novel Change Detection Framework Using Randomly Initialized Models With Spatial–Channel Augmentation for Hyperspectral Images

Published:2023 Issue: Volume:61 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Yang Bin¹^ORCID,Mao Yin¹^ORCID,Liu Licheng¹^ORCID,Liu Xinxin¹^ORCID,Ma Yuzhong²,Li Jing³^ORCID

Affiliation:

1. College of Electrical and Information Engineering, Hunan University, Changsha, China

2. Shandong Provincial Institute of Land Surveying and Mapping, Jinan, China

3. School of Information, Central University of Finance and Economics, Beijing, China

Funder

Changsha Science and Technology Program

National Key Research and Development Program of China

Key Laboratory of Jiangxi Province for Persistent Pollutants Control and Resources Recycle

2022 Cotton Science and Technology Innovation Project of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10086615.pdf?arnumber=10086615

Reference67 articles.

1. Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies

2. Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images

3. Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta

4. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

5. RSCNet: A Residual Self-Calibrated Network for Hyperspectral Image Change Detection

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