Remote sensing image segmentation method based on hard boundary constraint and two-stage combination

A technology of remote sensing images and hard boundaries, applied in image analysis, image data processing, instruments, etc.

Inactive Publication Date: 2013-07-17
NANJING NORMAL UNIVERSITY
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Problems solved by technology

However, this type of method has further exploration in the balance of noise and weak edges, the information complementary mechanism of edges and regions, the balance of over-segmen

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  • Remote sensing image segmentation method based on hard boundary constraint and two-stage combination
  • Remote sensing image segmentation method based on hard boundary constraint and two-stage combination
  • Remote sensing image segmentation method based on hard boundary constraint and two-stage combination

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Embodiment

[0061] Data preparation: The image segmentation test is carried out using true-color aerial images with red, green, and blue bands, and the spatial resolution of the image is 0.3 meters. The image size is 664×905 pixels. The parameters of Canny edge extraction are set as Gaussian filter standard deviation is 0.5; the ratio of high threshold is 0.7; the ratio of low threshold to high threshold is 0.6. The hard border ratio is 0.3, that is, the hard border ratio of two tiles cannot be merged if it is greater than 30%. T_Max is set to 1000. The minimum unconstrained scale parameter T_Scale is set to 10, and other scale segmentation results are merged through 10 scale unconstrained. Other parameters required by the method of the present invention, spectral heterogeneity weight w is 0.7, compactness heterogeneity weight w cmpct It is 0.5, which is the default parameter setting of eCognition5.0 software. The detailed process of case implementation is as follows:

[0062] The fi...

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Abstract

The invention discloses a remote sensing image segmentation method based on hard boundary constraint and two-stage combination. The method mainly includes the steps that first watershed segmentation of edge constraint and edge distribution are conducted to obtain sub-elements; and then layered sub-elements of the edge constraint are combined, the sub-elements can conduct extreme growth under the edge control, and a so-called initial element set is obtained; and on the basis of the two steps, the edge constraint is abandoned, secondary element layer combination is conducted to obtain a final segmentation result. In the two combining processes, an element combining strategy capable of combining elements repeatedly is designed so as to quicken the combining processes. Compared with a Fractal Net Evolution Approach (FNEA) method of eCognition software, the remote sensing image segmentation method has the advantage that edge position precision of the elements is high; mistaken segmentation rate is slightly small, over-segmentation rate is remarkably reduced; and dependency degree on scale parameters is low.

Description

technical field [0001] The invention relates to a remote sensing image segmentation method, in particular to a remote sensing image segmentation method based on hard boundary constraints and two-stage merging, and belongs to the field of remote sensing image processing and information extraction. Background technique [0002] High-resolution remote sensing images present more details of ground objects, but on the other hand, the distribution and structure of ground objects are very complex, which brings about serious spectral confusion, mutual occlusion of ground objects, shadows, and obvious noise interference. Meta-image analysis techniques pose serious challenges. Different from traditional pixel-oriented analysis methods, the smallest unit of image analysis by object-oriented remote sensing image analysis technology is no longer a single pixel, but a group of pixels that are related to each other (also called feature primitives, that is, The basic unit for feature extra...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 汪闽叶鹏
Owner NANJING NORMAL UNIVERSITY
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