DNN-Pol SAR: Urban Image Segmentation and Classification using Polarimetric SAR based on DNNs
Soumyadip Sarkar1, Farhan Hai Khan2, Shobhit Kumar3, Tamesh Halder4, Dipjyoti Paul5, Debashish Chakravarty6
1Soumyadip Sarkar, Institute of Engineering & Management, Kolkata, India.
2Farhan Hai Khan, Institute of Engineering & Management, Kolkata, India.
3Shobhit Kumar, Institute of Engineering & Management, Kolkata, India.
4Tamesh Halder, Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India.
5Dipjyoti Paul, Department of Computer Science, University of Crete, Heraklion, Crete, Greece.
6Debashish Chakravarty, Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India.
Manuscript received on 12 April 2024 | Revised Manuscript received on 11 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 1-13 | Volume-11 Issue-5, May 2024 | Retrieval Number: 100.1/ijies.E444813050624 | DOI: 10.35940/ijies.E4448.11050524
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Synthetic Aperture Radar (SAR) image segmentation and classification is a popular technique for learn- ing and detection of objects such as buildings, trees, monuments, crops water-bodies, hills, etc. SAR technique is being used for urban development and city-planning, building control of municipal objects, searching best locations, detection of changes in the existing systems, etc. using polarimetry based on Deep Neural Networks. In this paper, weproposed a technique for Urban Image Segmentation and Classification using Polarimetric SAR based on Deep NeuralNetworks (DNN-PolSAR). In our proposed DNN-PolSAR technique, we useMask-RCNN, LinkNet, FPN, and PSP- Net as model architectures, whereas ResNet50, ResNet101, ResNet152, and VGG-19 are used as backbone networks.We first apply polarimetric decomposition on airborne Uninhabited Aerial Vehicle Synthetic Aperture (UAVSAR) im- ages of urban areas and then the decomposed images are fed to DNNs for segmentation and classification. We then simulate DNN-PolSAR considering different hyper-parameters and compare the obtained scores of hyper-parametersagainst used model architectures and backbone networks. In comparison, it is found that DNN-PolSAR based on FPNmodel with ResNet152 performed the best for segmentation and classification. The mean Average Precision (mAP) score of the DNN-PolSAR based on FPN with a pixel accuracy of 90.9% is 0.823, which outperforms other Deep Learning models.
Keywords: Polarimetric SAR, FPN, PSPNet, Mask-RCNN, LinkNet, Image Segmentation.
Scope of the Article: Classification