Superpixel-based principal component analysis for high resolution remote sensing image classification

In object-based image analysis (OBIA), it is often difficult to select the most useful features from a large number of segment-based information. The problem of choosing superpixel-based features is also very challenging. In order to solve this issue, this paper proposes a principal component analysis (PCA)-based method for superpixel-based classification of high resolution remote sensing imagery. This technique transforms the spectral features of superpixels, and the resulted feature variables are used to train a support vector machine classifier. Experiments based on 4 high resolution multispectral images indicated that although the performance is sensitive to the two parameters, the proposed method can increase classification accuracy effectively.