Semantic segmentation of PolSAR image data using advanced deep learning model

Abstract Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between h...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Rajat Garg, Anil Kumar, Nikunj Bansal, Manish Prateek, Shashi Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1cf38e28059342df904fa33136f6fc22
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1cf38e28059342df904fa33136f6fc22
record_format	dspace
spelling	oai:doaj.org-article:1cf38e28059342df904fa33136f6fc222021-12-02T18:47:04ZSemantic segmentation of PolSAR image data using advanced deep learning model10.1038/s41598-021-94422-y2045-2322https://doaj.org/article/1cf38e28059342df904fa33136f6fc222021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94422-yhttps://doaj.org/toc/2045-2322Abstract Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.Rajat GargAnil KumarNikunj BansalManish PrateekShashi KumarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rajat Garg Anil Kumar Nikunj Bansal Manish Prateek Shashi Kumar Semantic segmentation of PolSAR image data using advanced deep learning model
description	Abstract Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.
format	article
author	Rajat Garg Anil Kumar Nikunj Bansal Manish Prateek Shashi Kumar
author_facet	Rajat Garg Anil Kumar Nikunj Bansal Manish Prateek Shashi Kumar
author_sort	Rajat Garg
title	Semantic segmentation of PolSAR image data using advanced deep learning model
title_short	Semantic segmentation of PolSAR image data using advanced deep learning model
title_full	Semantic segmentation of PolSAR image data using advanced deep learning model
title_fullStr	Semantic segmentation of PolSAR image data using advanced deep learning model
title_full_unstemmed	Semantic segmentation of PolSAR image data using advanced deep learning model
title_sort	semantic segmentation of polsar image data using advanced deep learning model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1cf38e28059342df904fa33136f6fc22
work_keys_str_mv	AT rajatgarg semanticsegmentationofpolsarimagedatausingadvanceddeeplearningmodel AT anilkumar semanticsegmentationofpolsarimagedatausingadvanceddeeplearningmodel AT nikunjbansal semanticsegmentationofpolsarimagedatausingadvanceddeeplearningmodel AT manishprateek semanticsegmentationofpolsarimagedatausingadvanceddeeplearningmodel AT shashikumar semanticsegmentationofpolsarimagedatausingadvanceddeeplearningmodel
_version_	1718377667283124224

Semantic segmentation of PolSAR image data using advanced deep learning model

Ejemplares similares