Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality int...

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Autores principales:	Yufang He, Guangzong Zhang, Hermann Kaufmann, Guochang Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	SBAS-InSAR DCNN ResNet50 automatic interferogram selection Science Q
Acceso en línea:	https://doaj.org/article/a3de4dda87704f40ab06f4137a1c5d10
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spelling	oai:doaj.org-article:a3de4dda87704f40ab06f4137a1c5d102021-11-11T18:58:18ZAutomatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks10.3390/rs132144682072-4292https://doaj.org/article/a3de4dda87704f40ab06f4137a1c5d102021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4468https://doaj.org/toc/2072-4292The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore, the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring.Yufang HeGuangzong ZhangHermann KaufmannGuochang XuMDPI AGarticleSBAS-InSARDCNNResNet50automatic interferogram selectionScienceQENRemote Sensing, Vol 13, Iss 4468, p 4468 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	SBAS-InSAR DCNN ResNet50 automatic interferogram selection Science Q
spellingShingle	SBAS-InSAR DCNN ResNet50 automatic interferogram selection Science Q Yufang He Guangzong Zhang Hermann Kaufmann Guochang Xu Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks
description	The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore, the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring.
format	article
author	Yufang He Guangzong Zhang Hermann Kaufmann Guochang Xu
author_facet	Yufang He Guangzong Zhang Hermann Kaufmann Guochang Xu
author_sort	Yufang He
title	Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks
title_short	Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks
title_full	Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks
title_fullStr	Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks
title_full_unstemmed	Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks
title_sort	automatic interferogram selection for sbas-insar based on deep convolutional neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a3de4dda87704f40ab06f4137a1c5d10
work_keys_str_mv	AT yufanghe automaticinterferogramselectionforsbasinsarbasedondeepconvolutionalneuralnetworks AT guangzongzhang automaticinterferogramselectionforsbasinsarbasedondeepconvolutionalneuralnetworks AT hermannkaufmann automaticinterferogramselectionforsbasinsarbasedondeepconvolutionalneuralnetworks AT guochangxu automaticinterferogramselectionforsbasinsarbasedondeepconvolutionalneuralnetworks
_version_	1718431663393865728

Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

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