Post-classification smoothing of digital classification map of St. Louis, Missouri

We applied several majority kernels to land classification maps of St. Louis, Missouri. For our study site, the Landsat classification images are further processed by four techniques: (1) morphological filtering, (2) majority filtering by uniform square kernel, (3) majority' filtering by uniform circular kernel, (4) majority filtering by weighted circular kernel. The relationship between the kernel size and the overall land classification is investigated. For our study site, the results show that the majority' filtering using weighted circular kernel increases the overall classification accuracy by 18.8% compared to the raw classified image. This paper discusses the techniques and results of our study