Abstract
The popularity of deep learning has grown significantly among various researchers worldwide. Different deep learning models have been adopted in multiple applications wherein appreciable results are witnessed. However, several new models are yet to be explored for SAR image classification. Classification of SAR images are still suffering from issues such as misclassification or faulty predictions due to unreadable quality of images acquired by SAR systems, resulting in erroneous outcomes. This work focuses on applying one of the recent deep learning models called DenseNet to SAR image classification. Based on the study and experimental analysis carried in this work, a modified version of DenseNet called DenseNet179 is proposed in this work, by incorporating two types of dense blocks in the architecture of the model: one with the usual convolution, while the second with the depthwise convolution. The model is implemented and tested using the MSTAR benchmark acquired by the X-band SAR sensor. Results show that the incorporation of depthwise convolutions enables advanced feature learning of the model, with not as many parameters compared to all the DenseNet variants. The accuracy achieved on the new model is \(93.9\%\) which is higher than any of the variants of DenseNet implemented in this work for SAR image classification and outperforms various existing methods such as ATR-CNN and CDSPP.
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Passah, A., Kandar, D. (2023). Classification of Synthetic Aperture Radar Images Using a Modified DenseNet Model. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_46
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