Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a classification method based on multi-layer network and transfer learning has been developed for synthetic aperture radar (SAR) images inspired by recent successful deep learning methods. Multi-layer network has excellent performance in the classification of optical images, while its application for SAR images is restricted by the limited quantity of SAR imagery training data. Given this, transfer learning has been introduced into the classification of a small number of SAR images. Firstly,  use CIFAR-10 dataset to train a multi-layer network in order for an extraction of the mid-level representation, and then  utilize the intermediate layers of the network trained before to target SAR datasets at which the mid-level representation obtained can be used to train adaptive layers. The classification algorithm has been tested on a TerraSAR dataset and the results are more convincing and show greater potential for SAR image classification.
['Chenyao Kang', 'Chu He']
I am writing a research paper on a idea SAR image classification based on the multi-layer network and transfer learning of mid-level representations
[{"from": "human", "value": "I am writing a research paper on a idea SAR image classification based on the multi-layer network and transfer learning of mid-level representations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a classification method based on multi-layer network and transfer learning has been developed for synthetic aperture radar (SAR) images inspired by recent successful deep learning methods. Multi-layer network has excellent performance in the classification of optical images, while its application for SAR images is restricted by the limited quantity of SAR imagery training data. Given this, transfer learning has been introduced into the classification of a small number of SAR images. Firstly,  use CIFAR-10 dataset to train a multi-layer network in order for an extraction of the mid-level representation, and then  utilize the intermediate layers of the network trained before to target SAR datasets at which the mid-level representation obtained can be used to train adaptive layers. The classification algorithm has been tested on a TerraSAR dataset and the results are more convincing and show greater potential for SAR image classification."}]