Scale computation on high spatial resolution remotely sensed imagery multi-scale segmentation

Scale computation for multi-scale image segmentation is an important research area in geographic object-based image analysis (GEOBIA). For the highly spectral heterogeneity of high spatial resolution remotely sensed imagery (HSRRSI), and the changing sizes of geographic features and their spatially distributive patterns, it is difficult to build a global or local-scale calculation parameter model to effectively guide multi-scale segmentation parameters setting in large-scale regions. Usually, the segmentation parameters are used to measure the heterogeneous and homogenous adjacent pixels in spatial and spectral spaces simultaneously. It has been proved that the adaptive acquiring parameter of scale plays a key role in gaining precise segmentation results, and later it deeply influences the automatic recognition and post-processing of the physical image parcels (PIPs). However, in most cases, scale computation techniques still fail to guide segmentation to produce appropriate or repeatable results which should meet the practical production standard of GIS data based on GEOBIA. These techniques have not been summarized and classified and there is no review focusing on scale computation for HSRRSI multi-scale segmentation. We provide an overview of the state-of-the-art segmentation scale computation techniques which are mainly based on the spectral statistics and geometric characteristics, etc. Moreover, the pedigree of segmentation scale has been first time proposed, and the overall performance of each category is analysed. Especially, the methods of local variance, semivariance, and synthetic semivariance are presented. Then, the scale object selection (SOS) algorithm, spectral angle algorithm, and the RMAS (ratio of mean difference to neighbours (ABS) to standard deviation) are discussed at spectral domain. In addition, miscellaneous scale computation approaches are recognized as the important researching aspect. In order to clearly describe the scale computation on multi-scale image segmentation, we have proposed the new conceptions of semantic image object (SIO), PIP, particular scale of interest, symbiotic scale, etc. At last, the trends of scale computation for HSRRSI multi-scale segmentation also have been presented.