Land cover classification of SPOT image by local majority voting

We proposed a hierarchy scheme for the SPOT image land cover classification. In the first level, we combine the statistical classifier, maximum likelihood classification (MLC); the neural network classifier, learning vector quantization (LVQ); and use a 3×3 window to extract second-order statistical features to classify the image. If the pixel can´t reach the same label in this stage, it is processed in the second level. In the second stage, the first-order statistical features of each point in a window region are extracted. Then, the majority voting is used to label the pixel, the central point of the window, which is unclassified in the first level