Multi-scale superposition segmentation method

Abstract

The invention discloses a multi-scale superposition segmentation method, which comprises the following steps that: images are subjected to multi-scale segmentation to obtain each scale object after the segmentation; a stable scale index is utilized for judging each scale object to determine the optimum scale object of each scale; the determined optimum scale object of each scale is projected to a single data layer; and the optimum scale objects projected onto the single data layer are merged. The multi-scale superposition segmentation method has the advantages that the segmentation object extraction is carried out by using real ground object matching based on each object boundary as targets; the multi-scale segmentation on the images is realized; the problems of over-segmentation and under-segmentation of objects in the same scale but different land coverage types and the problem of overlapping during the multi-scale object superposition are avoided; and the precision of the multi-scale segmentation is improved.

Description

technical field [0001] The invention relates to the field of satellite remote sensing monitoring in geography, in particular to a multi-scale superposition and segmentation method. Background technique [0002] The spectral characteristics of remote sensing images greatly affect the accuracy of land cover mapping. Different land cover types themselves have a high degree of internal heterogeneity and inter-class similarity in spectral characteristics. Even high-resolution images often reduce the classification accuracy due to the phenomenon of "same spectrum and different objects". For traditional pixel-based, single The classification method of scale spectrum is difficult to solve the problem of classification accuracy. [0003] Scale effects refer to the translation of information at different spatial and temporal scales or at different organizational levels. Based on the scale space theory and the perspective of geoscience processes, patterns and processes often have diff...

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Problems solved by technology

[0006] At present, the research on land cover classification using multi-scale object-oriented methods is just in its infancy, and the research on the scale deduction of land cover image object features still faces the following problems: (1) Due to the heterogeneity of spectral and spatial characteristics of land cover types The law and mechanism of scale changes of different land covers are not clear, and a robust and standard scale selection method has not yet been formed; (2) The characteristics of scale changes have not been fully explored, Classification mainly relies on two-dimensional spectral and geometric features, ignoring the use of longitudinal features in the process of scale deduction; (3) There is no correlation between multi-scale land cover classifications, and the final composite classification results are time-consuming due to the overlap of the respective classification results. Error transmission is generated, and the effect of multi-type and multi-scale land cover is not ideal

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