In the present study, we propose a new simple approach to reduce the data dimensionality in hyperspectral image data. The basic assumption here consists in assuming that a pixel's curve of spectral response, as defined in the spectral space by the recorded digital numbers (DNs) at the available spectral bands, can be segmented, and each segment can be replaced by a smaller number of statistics: mean and variance, describing the main characteristics of a pixel's spectral response. It is expected that this procedure can be accomplished without significant loss of information. The DNs at even spectral band are used to calculate a few statistics that would be used instead of the DNs themselves in the classification process. For the pixel's spectral curve segmentation, we propose tree sub-optimal algorithms that are easy to implement and also computationally efficient. Using a top-down strategy, the original pixel's spectral curve is sequentially segmented. Experiments using a parametric classifier are performed on an AVIRIS data set. Encouraging results have been obtained in terms of classification accuracy and execution time, suggesting the effectiveness of the proposed algorithms. The results suggest that the proposed algorithms can be faster and achieve a better accuracy than the classical Sequential Forward Selection (SFS) technique, known from literature as one of the simplest and fastest techniques for data dimensionality reduction using the feature selection approach.
['Maciel Zortea', 'Victor Haertel']
Experiments on feature extraction in remotely sensed hyperspectral image data