Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new method for Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification based on Deep Sparse Filtering Network (DSFN) is proposed in this paper. It uses a novel deep learning network to learn features from the input raw data automatically. And the spatial information beten pixels on PolSAR image is combined into the input data. Moreover, unlike the conventional deep networks, the DSFN only needs to tune very few parameters during pre-training and fine-tuning. A real PolSAR data is used to verify the proposed method. Experimental results show that the proposed DSFN is efficient with less parameters and effectively improves the classification accuracy compared with conventional deep networks.
['Hongying Liu', 'Qiang Min', 'Chen Sun', 'Jin Zhao', 'Shuyuan Yang', 'Biao Hou', 'Jie Feng', 'Licheng Jiao']
I am writing a research paper on a idea Terrain classification with Polarimetric SAR based on Deep Sparse Filtering Network
[{"from": "human", "value": "I am writing a research paper on a idea Terrain classification with Polarimetric SAR based on Deep Sparse Filtering Network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new method for Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification based on Deep Sparse Filtering Network (DSFN) is proposed in this paper. It uses a novel deep learning network to learn features from the input raw data automatically. And the spatial information beten pixels on PolSAR image is combined into the input data. Moreover, unlike the conventional deep networks, the DSFN only needs to tune very few parameters during pre-training and fine-tuning. A real PolSAR data is used to verify the proposed method. Experimental results show that the proposed DSFN is efficient with less parameters and effectively improves the classification accuracy compared with conventional deep networks."}]