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.
Highlights
Synthetic aperture radar (SAR) is an active remote sensing technology that can gather ground information at any time and under any circumstances
We used regularization model with different accuracy we get in running our model
As shown in fig 22, the result obtained for agriculture and plantation classification is better as per the feature in the image so the model accuracy can improve with the use of different epochs
Summary
Synthetic aperture radar (SAR) is an active remote sensing technology that can gather ground information at any time and under any circumstances. It is essential to monitor urban change at spatial and temporal scales to monitor the changes in cities and their impact on natural assets and environmental systems. Analyse this urban feature using deep learning such as deep neural networks and the architecture has extracted the urban area. Synthetic aperture radar (SAR) has long been accepted in many remote sensing systems as an effective tool for urban data analysis due to its all-weather capability and depicting geometrical properties of the target. The deep learning algorithm is being used for the classification of the images and the backscatter signatures library.(Pottier 2011)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
