Feature extraction for hyperspectral image classification

• Published in: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) pp. 379 - 382
• Main Authors: Uddin, M. P., Mamun, M. A., Hossain, M. A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2017
• Subjects: Covariance matrices; Eigenvalues and eigenfunctions; Feature extraction; feature selection; FPCA; hyperspectral image classification; Hyperspectral imaging; KECA; Kernel; KPCA; PCA; Principal component analysis; SPCA
• ISSN: 2572-7621
• DOI: 10.1109/R10-HTC.2017.8288979

Remote sensing hyperspectral image (HSI) contains important information of ground surface as a set of hundreds of narrow and contiguous spectral bands. For effective classification of hyperspectral images, feature reduction techniques through feature extraction and feature selection approaches are applied to improve the classification performance. Principal Component Analysis (PCA) is the widely used feature extraction method for dimensionality reduction. In this paper, PCA and its linear variants such as segmented-PCA (SPCA) and folded-PCA (FPCA) together with nonlinear variants kernel-PCA (KPCA) and Kernel Entropy Component Analysis (KECA) have been studied to effectively extract the features for classification task. The feature selection over the new transformed features was carried out using cumulative-variance accumulation based approach except for KECA that employs Renyi entropy based feature selection. The studied methods are compared using real hyperspectral image. The experimental result shows that the classification accuracy of KPCA (95.9245%) and KECA (95.6262%) outperforms FPCA (95.1292%). However, the FPCA provides the less space complexity.