Deep Learning Algorithm for Urban Feature Extraction Using Sar Data

Abstract

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.

A method for transforming 2D imaging features with the AFSS‐loaded rotating Luneberg lens reflector

“…Synthetic aperture radar (SAR) can form two-dimensional (2D) imaging results of the desired target area with all-day, allweather, and high-resolution imaging capability [1][2][3]. The micro-motion target can modulate the non-stationary phase on the SAR echo signal due to micro-Doppler characteristics [4][5][6][7].…”