Improved object-oriented high-resolution remote sensing image classification method

Abstract

The invention provides an improved object-oriented high-resolution remote sensing image classification method. The method comprises the following steps: firstly, carrying out initial segmentation on an image by using an SLIC superpixel segmentation algorithm; enabling an initial segmentation result to generate over-segmentation; subjecting the initial segmented image to region fusion to solve anover-segmentation problembut a local under-segmentation phenomenon occurs in the image subjected to region fusion, so that secondary segmentation is carried out on the local under-segmentation region toobtain a new segmented image, the over-segmentation and under-segmentation phenomena in the segmentation process can be solved, and the image segmentation and image classification precision is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an improved object-oriented high-resolution remote sensing image classification method. Background technique [0002] With the application of object-oriented thinking in high-resolution remote sensing image processing, this method continues to develop and innovate, and has gradually become an inevitable choice for remote sensing image classification. In recent years, experts and scholars at home and abroad have continuously proposed high-resolution remote sensing image classification New ideas and algorithms. Therefore, the object-oriented high-resolution remote sensing image classification method has gradually replaced the pixel-based remote sensing image classification method, and has achieved remarkable results in the field of image classification. With the development of sensing technology and the updating of satellites, remote sensing images are widely used in vari...

AI Technical Summary

Problems solved by technology

Common segmentation algorithms will produce over-segmentation and under-segmentation. Under-segmentation of images will cause misclassification and reduce the accuracy of image classification.

Image over-segmentation will lead to an increase in the amount of calculations, and common image processing methods often segment the image in order to reduce the under-segmentation problem. Although the under-segmentation is reduced, the over-segmentation phenomenon increases, which greatly increases the amount of image calculations, resulting in an increase in computing speed.

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