Improving Principal Component Analysis Performance for Reducing Spectral Dimension in Hyperspectral Image Classification
- Resource Type
- Conference
- Authors
- Arsa, Dewa Made Sri; Sanabila, H. R.; Rachmadi, M. Febrian; Gamal, Ahmad; Jatmiko, Wisnu
- Source
- 2018 International Workshop on Big Data and Information Security (IWBIS) Big Data and Information Security (IWBIS), 2018 International Workshop on. :123-128 May, 2018
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Principal component analysis
Hyperspectral imaging
Feature extraction
Data mining
Support vector machines
Training data
Neural networks
- Language
High dimensional data will cause problems in classification tasks and storage. Hyperspectral image is a 3D image with hundreds of spectrals. A dimensional reduction method is required to solve problem. PCA is a well known and common method for reducing data dimension. However, the standard PCA needs to be extended in order to improve its ability in hyperspectral image classification. From the previous study, PCA have limited capability on extracting information from the spectral in hyperspectral image. In this study, we propose a new method to improve PCA performance: as a feature extraction method and a dimensional reduction method. We have inserted a hidden layer to transform the data into a new dimension and used PCA to extract the information. The experiment was conducted using Indian Pines hyperspectral image which contains 200 spectrals. We also use datasets from UCI repository such as Iris and Seed datasets. The results showed that the proposed method is able to increase the standard PCA performance and it is comparable to Nonlinear PCA.