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Terrain classification with Polarimetric SAR based on Deep Sparse Filtering Network | IEEE Conference Publication | IEEE Xplore
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Terrain classification with Polarimetric SAR based on Deep Sparse Filtering Network


Abstract:

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 ...Show More

Abstract:

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 between 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.
Date of Conference: 10-15 July 2016
Date Added to IEEE Xplore: 03 November 2016
ISBN Information:
Electronic ISSN: 2153-7003
Conference Location: Beijing, China

References

References is not available for this document.