RF-HoDRF: High-Order Hybrid Discriminative Random Field Improved by Two-Layer Random Forest for SAR Image Change Detection-Reference-Cited by-同舟云学术

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RF-HoDRF: High-Order Hybrid Discriminative Random Field Improved by Two-Layer Random Forest for SAR Image Change Detection

Published:2022 Issue: Volume:19 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Song Wanying¹^ORCID,Du Yu¹^ORCID,Wu Yan²^ORCID,Zhang Peng²^ORCID,Mao Kezhi³^ORCID

Affiliation:

1. School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, China

2. School of Electronics Engineering, Xidian University, Xi’an, China

3. School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU), Jurong West, Singapore

Funder

Natural Science Foundation of China

Scientific Research Plan Projects of Shaanxi Education Department

Ph.D. Scientific Research Foundation

China Postdoctoral Science Foundation

Natural Science Basic Research Plan in Shaanxi Province of China

Civil Space Thirteen Five Years Pre-Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9651998/09853517.pdf?arnumber=9853517

Reference14 articles.

1. Discriminative Random Fields

2. Nonstationary PolSAR Image Classification by Deep-Features-Based High-Order Triple Discriminative Random Field

3. Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

4. Land Use and Land Cover Area Estimates From Class Membership Probability of a Random Forest Classification

5. Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Siamese Network Based on Multiple Attention and Multilayer Transformers for Change Detection;IEEE Transactions on Geoscience and Remote Sensing;2023

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