7 November 2008 Semi-supervised classification for hyperspectral remote sensing image based on PCA and kernel FCM algorithm
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Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 71471I (2008) https://doi.org/10.1117/12.813255
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
Hyperspectral remote sensing image classification is a challenging task in remote sensing applications because this image always has some information redundancy and is easy to be affected by noise or lack of the separability. A semi-supervised classification method based on principal component analysis (PCA) method and kernel fuzzy C-means (KFCM) algorithm for hyperspectral remote sensing image is proposed in this paper. First the PCA method finds an effective representation of spectral signature in a reduced dimensional feature space. Then a semi-supervised kernel-based FCM algorithm, called SSKFCM algorithm by introducing semi-supervised learning technique and the kernel trick simultaneously into conventional fuzzy C-means algorithm, is introduced to classify the feature vectors. Finally numerical experiments are conducted on a hyperspectral remote sensing image that provides digital images of 80 spectral bands with wavelength rang from 455 nm to 1642 nm. Classification performance is estimated by classification accuracy and kappa coefficient. The simulation results show that the proposed approach can be effectively applied to hyperspectral remote sensing image classification.
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Xiaofang Liu, Xiaofang Liu, Binbin He, Binbin He, Xiaowen Li, Xiaowen Li, } "Semi-supervised classification for hyperspectral remote sensing image based on PCA and kernel FCM algorithm", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71471I (7 November 2008); doi: 10.1117/12.813255; https://doi.org/10.1117/12.813255
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