AUTOMATIC CLASSIFICATION METHODS OF HIGH-RESOLUTION SATELLITE IMAGES: THE PRINCIPAL COMPONENT ANALYSIS APPLIED TO THE SAMPLE TRAINING SET

摘要

In Remote Sensing the various bands of multispectral data have not the same relevance in order to identify pixels inside a specific land cover class. The band algebra combines different images in order to construct a new one that has many advantages from the point of view of image understanding or classification (e.g. pseudobands, resulting from the vegetation indices, are used with success for the classification of vegetated areas). The idea of this project was to define new pseudobands through the Principal Component Analysis (PCA) applied to the training sample set of specific classes. We used high resolution IKONOS Multispectral images to test this methodology. PCA was not applied to the whole image, but only to the pixels belonging to a specific class (training sample set). Eigenvectors have a dimension equal to four, like the number of the original bands (Red, Green, Blue and Near Infrared). We selected the Eigenvectors with the highest relevance for a specific class and applied the correspondent orthogonal linear transformation to the whole image in order to obtain the pseudobands containing the relevant information of the chosen class. The same transformation could be applied to the sample training set to obtain a new sample not influenced by the outlier pixels. Pseudobands were segmented by means of a threshold values based on the histograms of the training set Principal Component. A control sample data set was employed to validate the method by means of the Confusion Matrix. The resulting image can be used as mask for the feature segmentation of the selected class.