Back to Search
Start Over
Hyperspectral Remote Sensing Images Terrain Classification Based on PCA-KMFA
- Source :
- ICNC-FSKD
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- Hyperspectral remote sensing images (HRSIs) have the problems of high dimensionality, strong linear correlation among dimensions, and poor data separability, which result in low terrain recognition rate. A new feature extraction algorithm based on principal component analysis (PCA) and kernel marginal Fisher analysis (KMFA), namely PCA-KMFA, is presented. Firstly, PCA is used for removing the linear correlation redundancy among the dimensions, and then KMFA is used for extracting the nonlinear separable features in the resulting PCA feature space, Bayesian classifier is used for performing classification in the resulting PCA-KMFA subspace. Based on the experimental results of two airborne visible-infrared imaging spectrometer (AVIRIS) HRSIs, we can see that the presented PCA-KMFA subspace method is superior to the linear discriminant analysis (LDA) subspace method and marginal Fisher analysis (MFA) subspace method.
- Subjects :
- Computer science
Feature vector
0211 other engineering and technologies
Imaging spectrometer
Hyperspectral imaging
020206 networking & telecommunications
02 engineering and technology
Linear discriminant analysis
Naive Bayes classifier
Kernel (linear algebra)
Kernel (image processing)
Computer Science::Computer Vision and Pattern Recognition
Principal component analysis
0202 electrical engineering, electronic engineering, information engineering
Subspace topology
021101 geological & geomatics engineering
Remote sensing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
- Accession number :
- edsair.doi...........a83d10adeaf6d3989a7a2117d524005d