Published May 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

DNN-PolSAR: Urban Image Segmentation and Classification using Polarimetric SAR based on DNNs

Creators

  • 1. Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India.

Contributors

Contact person:

  • 1. Institute of Engineering & Management, Kolkata, India.
  • 2. Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India.
  • 3. Department of Computer Science, University of Crete, Heraklion, Crete, Greece.

Description

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.

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Additional details

Identifiers

DOI
10.35940/ijies.E4448.11050524
EISSN
2319-9598

Dates

Accepted
2024-05-15
Manuscript received on 12 April 2024 | Revised Manuscript received on 11May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024.

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