DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA
This paper aims to discusses the extraction of urban features from airborne NISAR (NASA-ISRO SAR) data using deep learning algorithm for a part of Ahmedabad City. NISAR data is acquired in two wavelength bands (L and S) in hybrid polarization i.e., RH and RV. This study has used level two data viz.,...
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2021-06-01
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Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/93/2021/isprs-archives-XLIII-B3-2021-93-2021.pdf |
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doaj-8dc1bffce9f4489dbaf5ff0a9ceb366f2021-06-29T01:30:05ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342021-06-01XLIII-B3-20219310110.5194/isprs-archives-XLIII-B3-2021-93-2021DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATAN. Pithva0A. Vyas1D. Rawal2V. Nizalapur3G. Jain4A. Das5Center for Applied Geomatics, CRDF, CEPT University, Ahmedabad 380009, IndiaCenter for Applied Geomatics, CRDF, CEPT University, Ahmedabad 380009, IndiaCenter for Applied Geomatics, CRDF, CEPT University, Ahmedabad 380009, IndiaCenter for Applied Geomatics, CRDF, CEPT University, Ahmedabad 380009, IndiaSpace Applications Centre (ISRO), Ahmedabad 380015, IndiaSpace Applications Centre (ISRO), Ahmedabad 380015, IndiaThis paper aims to discusses the extraction of urban features from airborne NISAR (NASA-ISRO SAR) data using deep learning algorithm for a part of Ahmedabad City. NISAR data is acquired in two wavelength bands (L and S) in hybrid polarization i.e., RH and RV. This study has used level two data viz., amplitude data. Pre-processing of NISAR data in L and S wavelength bands was carried out by using MIDAS, software developed and provided by the Space Applications Centre. Pre-processing viz., Speckle suppression using different filters in varying window sizes, radiometric and geometric calibration was performed. Variation of backscattering coefficient (Sigma- nought) in different wavelengths and polarizations for different land use features were analysed. NISAR data in conjunction with LISS 4 (5.8 m resolution) data is subjected to different fusion techniques. Qualitative and Quantitative analysis was carried out and Gram Schmidt technique was chosen for further analysis. Segmentation was performed to achieve better analysis of the fused image and the amplitude image. Lastly, a deep learning architecture was developed for the automatic classification of the image, and the Convolution Neural Network model was designed using mobile net and the regularization techniques. Deep learning architecture in conjunction with e-cognition developer was used for extracting urban features.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/93/2021/isprs-archives-XLIII-B3-2021-93-2021.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
N. Pithva A. Vyas D. Rawal V. Nizalapur G. Jain A. Das |
spellingShingle |
N. Pithva A. Vyas D. Rawal V. Nizalapur G. Jain A. Das DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
N. Pithva A. Vyas D. Rawal V. Nizalapur G. Jain A. Das |
author_sort |
N. Pithva |
title |
DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA |
title_short |
DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA |
title_full |
DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA |
title_fullStr |
DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA |
title_full_unstemmed |
DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA |
title_sort |
deep learning algorithm for urban feature extraction using sar data |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2021-06-01 |
description |
This paper aims to discusses the extraction of urban features from airborne NISAR (NASA-ISRO SAR) data using deep learning algorithm for a part of Ahmedabad City. NISAR data is acquired in two wavelength bands (L and S) in hybrid polarization i.e., RH and RV. This study has used level two data viz., amplitude data. Pre-processing of NISAR data in L and S wavelength bands was carried out by using MIDAS, software developed and provided by the Space Applications Centre. Pre-processing viz., Speckle suppression using different filters in varying window sizes, radiometric and geometric calibration was performed. Variation of backscattering coefficient (Sigma- nought) in different wavelengths and polarizations for different land use features were analysed. NISAR data in conjunction with LISS 4 (5.8 m resolution) data is subjected to different fusion techniques. Qualitative and Quantitative analysis was carried out and Gram Schmidt technique was chosen for further analysis. Segmentation was performed to achieve better analysis of the fused image and the amplitude image. Lastly, a deep learning architecture was developed for the automatic classification of the image, and the Convolution Neural Network model was designed using mobile net and the regularization techniques. Deep learning architecture in conjunction with e-cognition developer was used for extracting urban features. |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/93/2021/isprs-archives-XLIII-B3-2021-93-2021.pdf |
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AT npithva deeplearningalgorithmforurbanfeatureextractionusingsardata AT avyas deeplearningalgorithmforurbanfeatureextractionusingsardata AT drawal deeplearningalgorithmforurbanfeatureextractionusingsardata AT vnizalapur deeplearningalgorithmforurbanfeatureextractionusingsardata AT gjain deeplearningalgorithmforurbanfeatureextractionusingsardata AT adas deeplearningalgorithmforurbanfeatureextractionusingsardata |
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