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Semantic segmentation of PolSAR image data using advanced deep learning model.

Rajat Garg1, Anil Kumar2, Nikunj Bansal1

  • 1School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India.

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|July 29, 2021
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Summary
This summary is machine-generated.

Deep learning models, specifically DeepLabv3+, can accurately map urban areas using Synthetic Aperture Radar (SAR) data, outperforming traditional machine learning algorithms. This approach minimizes misclassification of vegetated urban areas, crucial for upcoming missions like NISAR.

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Artificial Intelligence

Background:

  • Urban area mapping using remote sensing is vital for land cover estimation and change detection.
  • A key challenge in Synthetic Aperture Radar (SAR) data analysis is distinguishing highly vegetated urban areas from actual vegetation, leading to misclassification.
  • This research serves as a precursor for the NASA-ISRO Synthetic Aperture Radar (NISAR) mission.

Purpose of the Study:

  • To minimize the misclassification of vegetated urban targets into vegetation class using deep learning.
  • To evaluate the performance of deep learning models against traditional machine learning algorithms for land use and land cover (LULC) classification using Polarimetric SAR (PolSAR) data.
  • To demonstrate the effectiveness of transfer learning with pre-trained deep learning models on limited SAR datasets.

Main Methods:

  • Implementation of three machine learning algorithms: Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM).
  • Application of a deep learning model, DeepLabv3+, for semantic segmentation of PolSAR data.
  • Utilizing transfer learning with a pre-trained DeepLabv3+ model on a small dataset.

Main Results:

  • DeepLabv3+ achieved the highest pixel accuracy (87.78%) and overall pixel accuracy (85.65%), outperforming ML algorithms.
  • Random Forest (RF) showed the best performance among ML algorithms with an overall pixel accuracy of 77.91%.
  • DeepLabv3+ recorded the highest precision for the urban class (0.9228), with SVM and RF showing comparable results (0.8977 and 0.8958, respectively).

Conclusions:

  • Pre-trained DeepLabv3+ models are effective for LULC classification using SAR data, even with limited datasets, through transfer learning.
  • Deep learning models offer a significant improvement over traditional machine learning algorithms for urban area mapping with PolSAR data.
  • The study highlights the potential of deep learning for enhancing the accuracy of urban remote sensing applications, particularly in preparation for the NISAR mission.