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Sparse Manifold-Regularized Neural Networks for Polarimetric SAR Terrain Classification | IEEE Journals & Magazine | IEEE Xplore
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Sparse Manifold-Regularized Neural Networks for Polarimetric SAR Terrain Classification


Abstract:

In this article, a new deep neural network based on sparse filtering and manifold regularization (DSMR) is proposed for feature extraction and classification of polarimet...Show More

Abstract:

In this article, a new deep neural network based on sparse filtering and manifold regularization (DSMR) is proposed for feature extraction and classification of polarimetric synthetic aperture radar (PolSAR) data. DSMR uses a novel deep neural network (DNN) to automatically learn features from raw SAR data. During preprocessing, the spatial information between pixels on PolSAR images is exploited to weight each data sample. Then, in the pretraining and fine-tuning, DSMR uses the population sparsity and the lifetime sparsity (dual sparsity) to learn the global features and preserves the local structure of data by neighborhood-based manifold regularization. The dual sparsity only needs to tune a few parameters, and the manifold regularization cuts down the number of training samples. Experimental results on synthesized and real PolSAR data sets from different SAR systems show that DSMR can improve classification accuracy compared with conventional DNNs, even for data sets with a large angle of incidence.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 31, Issue: 8, August 2020)
Page(s): 3007 - 3016
Date of Publication: 12 September 2019

ISSN Information:

PubMed ID: 31536019

Funding Agency:


References

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