Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification

@article{Gu2017MultipleKS,
  title={Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification},
  author={Yanfeng Gu and Qingwang Wang and Bingqian Xie},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2017},
  volume={55},
  pages={1085-1105}
}
To effectively learn heterogeneous features extracted from raw LiDAR point cloud data for landcover classification, a multiple kernel sparse representation classification (MKSRC) framework is proposed in this paper. In the MKSRC, multiple kernel learning (MKL) is embedded into sparse representation classification (SRC). The heterogeneous features are first extracted from the raw LiDAR point cloud data before classification. These features contain useful information from different dimensions… CONTINUE READING

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References

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Showing 1-10 of 56 references

Multisource Geospatial Data Fusion via Local Joint Sparse Representation

IEEE Transactions on Geoscience and Remote Sensing • 2016
View 6 Excerpts
Highly Influenced

Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

IEEE Transactions on Information Theory • 2007
View 8 Excerpts
Highly Influenced

Least Squares Support Vector Machine Classifiers

Neural Processing Letters • 1999
View 5 Excerpts
Highly Influenced

A Multilevel Point-Cluster-Based Discriminative Feature for ALS Point Cloud Classification

IEEE Transactions on Geoscience and Remote Sensing • 2016
View 2 Excerpts

Exploiting Joint Sparsity for Pansharpening: The J-SparseFI Algorithm

IEEE Transactions on Geoscience and Remote Sensing • 2016
View 1 Excerpt

Sparse Representation Denoising Framework for 3-D Building Reconstruction From Airborne LiDAR Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing • 2016
View 1 Excerpt