10.1109/IGARSS.2012.6351726International Geoscience and Remote Sensing Symposium (IGARSS)4264-4266IGRS
The KPCA algorithm is widely used for feature extraction of hyperspectral images. One of the disadva...
The KPCA algorithm is widely used for feature extraction of hyperspectral images. One of the disadva...
A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyp...
10.1109/IGARSS.2011.6049460International Geoscience and Remote Sensing Symposium (IGARSS)1759-1762IG...
Presented in a 3-D structure called hypercube, hyperspectral imaging (HSI) suffers from large volume...
Dimensionality reduction represents a critical preprocessing step in order to increase the efficienc...
Dimensionality reduction represents a critical preprocessing step in order to increase the efficienc...
International audienceHyperspectral imaging (HI) collects information from across the electromagneti...
International audienceHyperspectral imaging (HI) collects information from across the electromagneti...
International audienceHyperspectral imaging (HI) collects information from across the electromagneti...
International audienceHyperspectral imaging (HI) collects information from across the electromagneti...
Remote sensing data has known an explosive growth in the past decade. This has led to the need for e...
In this paper, we present a new parallel implementation of the Vertex Component Analysis (VCA) algor...
Remote sensing data has known an explosive growth in the past decade. This has led to the need for e...
In this paper, we present a new parallel implementation of the Vertex Component Analysis (VCA) algor...
The KPCA algorithm is widely used for feature extraction of hyperspectral images. One of the disadva...
The KPCA algorithm is widely used for feature extraction of hyperspectral images. One of the disadva...
A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyp...
10.1109/IGARSS.2011.6049460International Geoscience and Remote Sensing Symposium (IGARSS)1759-1762IG...
Presented in a 3-D structure called hypercube, hyperspectral imaging (HSI) suffers from large volume...
Dimensionality reduction represents a critical preprocessing step in order to increase the efficienc...
Dimensionality reduction represents a critical preprocessing step in order to increase the efficienc...
International audienceHyperspectral imaging (HI) collects information from across the electromagneti...
International audienceHyperspectral imaging (HI) collects information from across the electromagneti...
International audienceHyperspectral imaging (HI) collects information from across the electromagneti...
International audienceHyperspectral imaging (HI) collects information from across the electromagneti...
Remote sensing data has known an explosive growth in the past decade. This has led to the need for e...
In this paper, we present a new parallel implementation of the Vertex Component Analysis (VCA) algor...
Remote sensing data has known an explosive growth in the past decade. This has led to the need for e...
In this paper, we present a new parallel implementation of the Vertex Component Analysis (VCA) algor...
The KPCA algorithm is widely used for feature extraction of hyperspectral images. One of the disadva...
The KPCA algorithm is widely used for feature extraction of hyperspectral images. One of the disadva...
A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyp...
DOI
Add to ORKG