A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model

Author:

Zhang Jianlong1,Liu Yifan1,Wang Bin1ORCID,Chen Chen2ORCID

Affiliation:

1. School of Electronic Engineering, Xidian University, Xi’an 710071, China

2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China

Abstract

The mainstream methods for change detection in synthetic-aperture radar (SAR) images use difference images to define the initial change regions. However, methods can suffer from semantic collapse, which makes it difficult to determine semantic information about the changes. In this paper, we proposed a hierarchical fusion SAR image change-detection model based on hierarchical fusion conditional random field (HF-CRF). This model introduces multimodal difference images and constructs the fusion energy potential function using dynamic convolutional neural networks and sliding window entropy information. By using an iterative convergence process, the proposed method was able to accurately detect the change-detection regions. We designed a dynamic region convolutional semantic segmentation network with a two-branch structure (D-DRUNet) to accomplish feature fusion and the segmentation of multimodal difference images. The proposed network adopts a dual encoder–single decoder structure where the baseline is the UNet network that utilizes dynamic convolution kernels. D-DRUNet extracts multimodal difference features and completes semantic-level fusion. The Sobel operator is introduced to strengthen the multimodal difference-image boundary information and construct the dynamic fusion pairwise potential function, based on local boundary entropy. Finally, the final change result is stabilized by iterative convergence of the CRF energy potential function. Experimental results demonstrate that the proposed method outperforms existing methods in terms of the overall number of detection errors, and reduces the occurrence of false positives.

Funder

Key Research and Development Program of Shaanxi

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province of China

Xi’an Science and Technology Plan

Key Project on Artificial Intelligence of Xi’an Science and Technology Plan

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 9 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. Object-Oriented SAR Image Change Detection Based on Speckle Reducing Anisotropic Diffusion;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Real-time Image Enhancement for Emergency Rescue Scenarios in Smart Grids;2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI);2024-05-24

3. Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering;Remote Sensing;2024-05-23

4. Accurate Water Gauge Detection by Image Data Augmentation Using Erase-Copy-Paste;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2024-05-20

5. A Safe-Distance Control Scheme to Avoid New Infection Like COVID-19 Virus Using Millimeter-Wave Radar;IEEE Sensors Journal;2024-03-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3