Comparing the Effect of Aggregation Methods for Remote Sensing Image

Spatial data aggregation is widely practiced for "scaling-up" environmental analyses and modeling from local to regional or global scales.There is a growing literature reporting on the effects of data aggregation under the general topic of the scale effects.The measurements,such as mean,median,variance,RMSE,etc.,are used to evaluate the effect of different aggregation methods in the studies.However,these measurements based on statistics have not taken into account the spatial pattern of the remote sensing image.Despite this,the original image is set as the reference data and the aggregation method which can retain statistical characteristics of the original data better than the others is considered the desirable method.For scientific inquiry,aggregating data to a coarser resolution is often performed,because certain spatial patterns will not be revealed until the data are presented at a coarser scale.It is more reasonable that the image scanned directly from sensor at the same scale with the result resolution of aggregation operation used as reference data.The objective of this study is to compare and evaluate the effect of five aggregation methods based on new measurements and new comparing thought line.Spatial resolution and Structural SIMilarity(SSIM)are introduced as two new measurements to evaluate the effects of aggregation methods.In the five aggregation methods,except for Arithmetic Average Variability-Weighted(AAVW),Average,Bicubic,Bilinear and Nearest are commonly used for aggregating remotely sensed data.The AAVW method is a new aggregation method based on spatial variability-weighted principle.Spatial resolution is a basic attribute of image data.The technical spatial resolution of up-scaled image can be specified precisely and explicitly by the resolution of original image and the aggregation levels.The aggregation effects are evaluated by differences between the resolution of aggregated images and the technical spatial resolution.In this study,the spatial resolution was retrieved by using linear diffuse function.The motivation of SSIM is to find a more direct way to compare the structures of the reference and the distorted images.Natural image signals are highly structured: their pixels exhibit strong dependencies,especially when they are spatially proximate,and these dependencies carry important information about the structure of the objects in the visual scene.SSIM defines the structural information in an image as those attributes that represent the structure of objects in the scene,independent of the average luminance and contrast.Two study sites from the Xinjiang Province,China were used: The Urumchi international airport and Tianshan Mountain.For each study site,the TM and ETM+ images were acquired.The multi-spectral images of ETM+ were merged with its panchromatic image.Thus,the resolution of ETM+ multi-spectral images was enhanced to 15 m.The merged ETM+ multi-spectral images were then aggregated by the five aggregation methods mentioned above using 2-by-2-pixel window size.The up-scaled images in Urumchi international airport were selected for the experiment based on spatial resolution measurement.SSIM was used in comparison of effects of the five aggregation methods in Tianshan Mountain sub-images.Experimental evaluations were conducted based on the new thought line and measurements among five aggregation methods.The TM images were used as reference data.Experimental Results show that the Bicubic and Bilinear method presented more wonderful performance than the others.The validity of the two new measurements,which based on the new thought line,was also checked through comparing the effects of the different aggregation methods.Finally,the future research about using the new evaluation measurements is also proposed.