PolSAR image classification based on TCN deep learning: a case study of greater Cairo

ABSTRACT

Environmental applications play a significant role in the ongoing research area of Polarimetric Synthetic Aperture Radar (PolSAR) image classification. In this paper, a new model is proposed for classifying PolSAR images and applied to a part of the Greater Cairo area in the Nile basin, South of Delta, Egypt. First, the proposed model performs data pre-processing by extracting the coherency and covariance elements noted as [T] and [C] matrices, respectively. Second, temporal convolutional networks (TCN) deep learning is used to extract the features from coherency and covariance elements and then train the model. Third, the SoftMax classifier is used to classify the PolSAR image. Finally, the proposed model is tested with evaluation metrics. The obtained results show that the proposed model can achieve high classification performances.

KEYWORDS:

• Polarimetric synthetic aperture radar (PolSAR)
• temporal convolutional networks (TCN)
• deep learning
• PolSAR image classification

Disclosure statement

No potential conflict of interest was reported by the author(s).