Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images

Change detection for remote sensing images is an indispensable procedure for many remote sensing applications, such as geological disaster assessment, environmental monitoring, and urban development monitoring. Through this technique, the difference in certain areas after some emergencies can be det...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Puhua Chen, Lei Guo, Xiangrong Zhang, Kai Qin, Wentao Ma, Licheng Jiao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	change detection Siamese network remote sensing image attention mechanism Science Q
Acceso en línea:	https://doaj.org/article/96312b626ccf4a2fbb7f6eaa6b6359d3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:96312b626ccf4a2fbb7f6eaa6b6359d3
record_format	dspace
spelling	oai:doaj.org-article:96312b626ccf4a2fbb7f6eaa6b6359d32021-11-25T18:54:38ZAttention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images10.3390/rs132245972072-4292https://doaj.org/article/96312b626ccf4a2fbb7f6eaa6b6359d32021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4597https://doaj.org/toc/2072-4292Change detection for remote sensing images is an indispensable procedure for many remote sensing applications, such as geological disaster assessment, environmental monitoring, and urban development monitoring. Through this technique, the difference in certain areas after some emergencies can be determined to estimate their influence. Additionally, by analyzing the sequential difference maps, the change tendency can be found to help to predict future changes, such as urban development and environmental pollution. The complex variety of changes and interferential changes caused by imaging processing, such as season, weather and sensors, are critical factors that affect the effectiveness of change detection methods. Recently, there have been many research achievements surrounding this topic, but a perfect solution to all the problems in change detection has not yet been achieved. In this paper, we mainly focus on reducing the influence of imaging processing through the deep neural network technique with limited labeled samples. The attention-guided Siamese fusion network is constructed based on one basic Siamese network for change detection. In contrast to common processing, besides high-level feature fusion, feature fusion is operated during the whole feature extraction process by using an attention information fusion module. This module can not only realize the information fusion of two feature extraction network branches, but also guide the feature learning network to focus on feature channels with high importance. Finally, extensive experiments were performed on three public datasets, which could verify the significance of information fusion and the guidance of the attention mechanism during feature learning in comparison with related methods.Puhua ChenLei GuoXiangrong ZhangKai QinWentao MaLicheng JiaoMDPI AGarticlechange detectionSiamese networkremote sensing imageattention mechanismScienceQENRemote Sensing, Vol 13, Iss 4597, p 4597 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	change detection Siamese network remote sensing image attention mechanism Science Q
spellingShingle	change detection Siamese network remote sensing image attention mechanism Science Q Puhua Chen Lei Guo Xiangrong Zhang Kai Qin Wentao Ma Licheng Jiao Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images
description	Change detection for remote sensing images is an indispensable procedure for many remote sensing applications, such as geological disaster assessment, environmental monitoring, and urban development monitoring. Through this technique, the difference in certain areas after some emergencies can be determined to estimate their influence. Additionally, by analyzing the sequential difference maps, the change tendency can be found to help to predict future changes, such as urban development and environmental pollution. The complex variety of changes and interferential changes caused by imaging processing, such as season, weather and sensors, are critical factors that affect the effectiveness of change detection methods. Recently, there have been many research achievements surrounding this topic, but a perfect solution to all the problems in change detection has not yet been achieved. In this paper, we mainly focus on reducing the influence of imaging processing through the deep neural network technique with limited labeled samples. The attention-guided Siamese fusion network is constructed based on one basic Siamese network for change detection. In contrast to common processing, besides high-level feature fusion, feature fusion is operated during the whole feature extraction process by using an attention information fusion module. This module can not only realize the information fusion of two feature extraction network branches, but also guide the feature learning network to focus on feature channels with high importance. Finally, extensive experiments were performed on three public datasets, which could verify the significance of information fusion and the guidance of the attention mechanism during feature learning in comparison with related methods.
format	article
author	Puhua Chen Lei Guo Xiangrong Zhang Kai Qin Wentao Ma Licheng Jiao
author_facet	Puhua Chen Lei Guo Xiangrong Zhang Kai Qin Wentao Ma Licheng Jiao
author_sort	Puhua Chen
title	Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images
title_short	Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images
title_full	Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images
title_fullStr	Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images
title_full_unstemmed	Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images
title_sort	attention-guided siamese fusion network for change detection of remote sensing images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/96312b626ccf4a2fbb7f6eaa6b6359d3
work_keys_str_mv	AT puhuachen attentionguidedsiamesefusionnetworkforchangedetectionofremotesensingimages AT leiguo attentionguidedsiamesefusionnetworkforchangedetectionofremotesensingimages AT xiangrongzhang attentionguidedsiamesefusionnetworkforchangedetectionofremotesensingimages AT kaiqin attentionguidedsiamesefusionnetworkforchangedetectionofremotesensingimages AT wentaoma attentionguidedsiamesefusionnetworkforchangedetectionofremotesensingimages AT lichengjiao attentionguidedsiamesefusionnetworkforchangedetectionofremotesensingimages
_version_	1718410603539726336

Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images

Ejemplares similares