Principal Component Analysis for Hyperspectral Image Classification
Product Description
Principal Component Analysis for Hyperspectral Image Classification
Abstract— we have applied supervised probabilistic principal component analysis (SPPCA) and semi-supervised probabilistic principal component analysis (S2PPCA) for feature extraction in hyperspectral remote sensing imagery.< Final Year Projects > The two models are all based on probabilistic principal component analysis (PPCA) using EM learning algorithm. SPPCA only relies on the labeled samples into the projection phase, while S2PPCA is able to incorporate both the labeled and unlabeled information. Experimental results on three real hyperspectral images demonstrate the SPPCA and S2PPCA outperform some conventional feature extraction methods for classifying hyperspectral remote sensing image with low computational complexity.
Video
View Demo
Including Packages
Our Specialization
Support Service
Statistical Report
satisfied customers
3,589
Freelance projects
983
sales on Site
11,021
developers
175+
Additional Information
| Domains | |
|---|---|
| Programming Language |
Reviews
There are no reviews yet.