The Study of Complex Valued (CV) Deep Convolutional Networks
DuÅ¡an Gleich, Danijel SipoÅ¡, Peter PlaninÅ¡i
Journal of Networking Technology, Vol. 12, No. 3, 2021.
DOI: https://doi.org/10.6025/jnt/2021/12/3/72-76
Link: https://www.dline.info/jnt/fulltext/v12n3/jntv12n3_2.pdf
Abstract:
For classifying the synthetic aperture radar data, we have developed a model for comparing complex valued (CV) deep Convolutional Networks (CNN). This model has feature extraction and classification of synthetic aperture radar patches. The image segmentation with deep learning is more popular as it can able to handle large databases and is welldeveloped. In this work we have introduced deep convolutional networks for Synthetic Aperture Radar patch classification.
The experimentation was conducted with convolutional networks of 20 layers. The available layers are complex valued input layer, output layer and one or more hidden layers in the CNN. The Hidden layers consists of any combination of convolutional layers, pooling layers, activation functions, and are fully defined within complex valued domain. We have designed a
model database of patches with three classes and parameters and are tested.