Nonlinear Feature Extraction for Hyperspectral Images

Abstract

In this study non-linear dimension reduction methods have been applied to a hyperspectral image in order to increase the classification accuracy in feature extraction step. Furthermore, image segmentation has been ensured the by taking into consideration the spatial synthesis of hyperspectral images and passing from high-dimensional space to low dimensional space. It has been compared the results obtained from the image segmentation made by taking one pixel from this spatial synthesis. The advantages of the effects of the results of the dimension reduction techniques made by facing neighbor pixels on the segmentation of hyper-spectral image have been displayed in the experimental results part.

Keywords

• feature extraction,spatial dependency; dimension reduction; manifold learning.

References

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