Multi-spectral Image Analysis Based on Dynamical Evolutionary Projection Pursuit
نویسندگان
چکیده
Principal component analysis (PCA) is usually used for compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of data variance. PCA is effective if the multivariate data set is a vector with Gaussian distribution. But multi-spectral images data sets are not probably submitted to such Gaussian distribution. The paper proposes a method based on Projection Pursuit to find a set of projections that are “interesting”, in the sense that they deviate from Gaussian distribution. Also a dynamical evolutionary algorithm was developed in order to find the optimal projection index. The effectiveness of this method is demonstrated through simulated data and multi-spectral image data.
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