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Forest Classification Using Simulated Compact Polarimetry Data and Deep Learning Networks
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Sahar Ebrahimi *   , Hamid Ebadi , Amir Aghabalaei |
| K. N. Toosi University of Technology |
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Abstract: (1520 Views) |
| In the last two decades, among various Synthetic Aperture RADAR (SAR) imaging modes, Compact Polarimetry (CP) mode has come to attention due to less complex imaging system, mass and data rate reduction, and also greater swath width. Having such advantages makes this data very useful for large-scale target mapping, such as forest classification. Different methods have been proposed for forest classification using CP mode, which all of them are based on feature extraction. The accuracy of these methods depends on the discrimination of the extracted features. In the meantime, deep learning networks have almost automated the feature extraction phase and obtained impressive results, especially in the classification task. In this paper, the ability of deep learning networks using CP mode data in forest classification is investigated. The study area of this paper is Petawawa forest located in Ontario, Canada, and the data being used are simulated CP data, Full Polarimetric (FP) data, and also reconstructed Pseudo Quad (PQ) data acquired from RADARSAT-2 in C-band. The proper deep learning network for automatic feature extraction is designed and the classification is performed on CP, FP, and PQ data. The results from each mode classification are compared and evaluated with each other and also with the results from Wishart classifier and Support Vector Machine (SVM). The results of this paper show that using deep learning networks improves the classification accuracy of CP and PQ modes. |
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| Keywords: SAR, Compact Polarimetry, Convolutional Neural Networks, Forest Classification |
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Full-Text [PDF 2364 kb]
(397 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2020/12/26 | Accepted: 2021/09/7 | ePublished ahead of print: 2023/06/21 | Published: 2023/07/9
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