Super pixel based remote sensing image classification with histogram descriptors on spectral and spatial data

Categorization based on objects is an effective way to integrate spectral and spatial information into remote sensing image classification. In this paper, we establish a classification framework which represents objects by super pixels. The non-parametric k-NN approach is chosen for this super pixel based method, as it is simple and free of class data distribution. A new descriptor for the features distribution of each super pixel, called 4-D color histograms, is used for both spectral and texture information. This descriptor provides a better tolerance for value fluctuations inside the super pixel. Furthermore, the Ç₂ distance is used as the measure of the similarity between color histograms of the super pixels. Experiments are conducted to illustrate the application of the proposed method.