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 C¸2 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.