Evaluation of spatial aggregation methods based on satellite classification data

Abstract

Spatial aggregation methods are widely used in spatial upscaling to derive remote sensing classification data of desired resolution as the input of environmental analyses and modeling, which also, on the other hand, bring different levels of information loss. At present, systematic assessment of these spatial aggregation methods is mainly focused on the retention capacity of original image information, but less on the similarity between aggregated classification data and the satellite classification data of the same scale. This paper proposed an index and quantitatively evaluated three widely used spatial aggregation methods-cell-center, maximum-area, median-based on the latter evaluation method. The 0.3m high resolution classification data photographed by airship were aggregated to 30m using the above three aggregation methods and then were compared with TM classification data. Results show that maximum-area aggregated data are the closest because this method takes into account all the pixels in the window and it can be better applied to the aggregation of satellite classification data.