Remoto sensing image space shape characteristics extracting and sorting method

Abstract

One picking-up and classify method for remote sensing picture space figure character, tests the space figure structure character of pixel by a series of direction line in equal interval surrounding with center pixel. The amount of direction line is 5 to 48, and its length is controlled by Inter-item consistency referenced threshold and the biggest length referenced threshold and different with each other to embody the anisotropic of video. The square diagram of pixel's direction line reflects its structure character for picking-up space figure structure character more effectively. It reduces the dimensional number of character, and adopts length, width, pixel figure exponent, ratio of length and wide, weighted means and variance to pick-up the square diagram character of direction line to each pixel. It adopts spectrum and space structure character amalgamation classified method to select one method of many kind of neural network and machine study arithmetic to dispose high dimensional structure space.

Description

technical field [0001] The invention belongs to the technical field of computer remote sensing image processing and pattern recognition, and is a new method of extracting image shape and structure features by using the context spectrum similarity distribution of remote sensing images, and classifying high-dimensional spectral and spatial features with neural networks and support vector machines Methods. Background technique [0002] High-spatial-resolution remote sensing images can provide a large number of surface features, and the rich detailed information of the internal components of the same object category can be represented, the spatial information is more abundant, and the size, shape, and relationship between adjacent objects are better reflected. . However, the spectral statistical characteristics of this new type of remote sensing images are not as stable as those of low-resolution images, and the spatial distribution of ground objects is complex, and similar obj...

AI Technical Summary

Problems solved by technology

However, the spectral statistical characteristics of this new type of remote sensing images are not as stable as those of low-resolution images, and the spatial distribution of ground objects is complex, and similar objects show great spectral heterogeneity, which is manifested in the increase of intra-class variance and the decrease of inter-class variance. The spectra of different ground objects overlap each other, so that the traditional spectral classification method cannot obtain satisfactory results

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