The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 93–101, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-93-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 93–101, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-93-2021

  28 Jun 2021

28 Jun 2021

DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA

N. Pithva1, A. Vyas1, D. Rawal1, V. Nizalapur1, G. Jain2, and A. Das2 N. Pithva et al.
  • 1Center for Applied Geomatics, CRDF, CEPT University, Ahmedabad 380009, India
  • 2Space Applications Centre (ISRO), Ahmedabad 380015, India

Keywords: SAR, Deep Learning, Convolution Neural Network, Feature Extraction, Segmentation

Abstract. 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.