Optimum segmentation dimension determining method for remotely-sensed image land cover classification

Abstract

The invention provides an optimum segmentation dimension determining method for remotely-sensed image land cover classification. The method comprises steps of multi-dimension segmentation and classification of remotely-sensed images and optimum dimension selection based on entropy information. The method performs remotely-sensed image classification by integrating a pixel-based classification method and an object-oriented classification method and by fully utilizing sample information, effectively overcomes a problem that a conventional pixel-oriented method generates a large amount of pepper salt noisy points, and achieves automatic selection of object optimum dimension. The invention provides an effective method for land cover drawing.

Description

technical field [0001] The invention relates to a processing method for remote sensing images, in particular to a method for determining an optimal segmentation scale for land cover classification of remote sensing images, and belongs to the field of image processing. Background technique [0002] Land cover refers to the complex of surface elements covered by natural structures and artificial buildings, including surface vegetation, soil, lakes, swamps and various buildings (such as roads, houses, etc.). Land cover is an important forcing factor of global environmental change, which has attracted more and more researchers' attention in recent years. [0003] With the development of remote sensing science and technology, the resolution of remote sensing images is getting higher and higher, which provides feasibility for the mapping of land cover on multiple spatial scales. The classification of remote sensing images is an important link in land cover mapping, which determin...

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Problems solved by technology

However, in most cases, the objects in an image are of different sizes, and it is obviously unreasonable to apply the same segmentation scale to objects of different sizes for segmentation

In addition, T.Esch (2008) proposed a method to automatically determine the optimal segmentation scale for different objects, see Esch T, Thiel M, Bock M, et al.Improvement of image segmentation accuracy based on multiscale optimization procedure[J ].Geoscience and Remote Sensing Letters, IEEE,2008,5(3):463-467. However, a large number of parameters are used to determine the optimal scale, which makes the whole algorithm quite complicated

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