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We put forward a novel graph construction method for semi-supervised learning, called SLIC Superpixel-based l 2 , 1 -norm Robust Principal Component Analysis ( ...
Dimensionality reduction has become an important means to overcome the "Curse of dimensionality". In hyperspectral images, labeled samples are ...
Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades.
Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, ...
This paper proposes a superpixel/voxel medical image segmentation algorithm based on regional interlinked value and block (region) merging, which can segment ...
A novel classification method using ℓ2,1ℓ2,1-norm based regression is proposed in this paper. The ℓ2,1ℓ2,1-norm based loss function is robust to outliers or ...
In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering ...
Sep 30, 2015 · ... SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification", Sensors, 19, 3:. doi:10.3390 ...
The improved SLIC, specifically designed for HSI, can straightly segment HSI with arbitrary dimensionality into superpixels, without consulting principal ...
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2019: SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification Sensors 19(3) · Sun, W.; Yang, G.; Peng, J ...