Remote sensing sub-pixel mapping method based on spatial distribution characteristics of features

Abstract

The invention discloses a remote sensing sub-pixel mapping method based on the spatial distribution characteristics of features, comprising the following steps: first, classifying remote sensing images into an area pattern, a line pattern and a point pattern according to the spatial geometric characteristics of features; then, under the hypothesis of spatial dependence, using a sub-pixel mapping method based on vector boundary to process area features, using a line feature template sub-pixel mapping method to process line features, and using a sub-pixel mapping method based on space pattern consistency matching to process point features; and finally, embedding the sub-pixel mapping results of the three spatial patterns to get a sub-pixel map of the images. According to the invention, a sub-pixel mapping theoretical model capable of processing point, line and area features at the same time and based on the spatial distribution characteristics of features is built theoretically, and the simulation precision is high.

Description

technical field [0001] The invention relates to a sub-pixel mapping method, which belongs to the technical field of geospatial information. Background technique [0002] Remote sensing science and technology have been widely and deeply applied in fields such as land resource survey, hydrological process simulation, landscape ecology, agricultural yield estimation and disaster monitoring, forestry resource management, global climate change (Mei Anxin, 2001; Zhou Chenghu et al., 1999), high space Remote sensing imagery and its products with high, temporal and spectral resolutions are the development trend for further research and application in various fields (Zhao Yingshi, 2003). However, in the process of remote sensing image acquisition, due to the influence of the external environment and the limitations of the internal sensor itself, mixed pixels (MixedPixel) generally exist in the image, which limits the application of remote sensing images and restricts the extraction o...

AI Technical Summary

Problems solved by technology

Although the sub-pixel mapping algorithm based on the largest spatial correlation has a good processing effect on surface objects, it will cause the aggregation of point objects when dealing with point objects with heterogeneous distribution characteristics, and When dealing with linear features, it is often difficult to ensure the connectivity and spatial distribution characteristics of linear features, and it is easy to produce "sawtooth effect".

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