New noise-containing remote sensing image segmentation method

Abstract

The invention provides a noise-containing remote sensing image segmentation algorithm which is based on wavelet packet analysis and is combined with improved 2D otsu, aiming at improving noise-containing image segmentation accuracy. The algorithm includes that firstly wavelet packet is used for decomposing a noise-containing remote sensing image, then adaptive threshold method is adopted to remove noise in the image, and then the two-dimensional histogram of the image is projected onto diagonal, so as to form a one-dimensional histogram with obvious 'double peak' distribution, and finally 2D otsu method is applied for searching optimal segmentation threshold on a line vertical to the diagonal, so as to realize image segmentation. The invention not only effectively removes interference of noise but also obtains better segmentation effect.

Description

A New Method for Segmentation of Noisy Remote Sensing Images technical field The invention relates to an improved 2D Otsu segmentation method of a noisy remote sensing image, belonging to the technical field of remote sensing. Background technique Image segmentation is one of the most important contents in the field of image processing, and it is the basis for image analysis and understanding. Image segmentation can be understood as extracting meaningful feature areas in the image or feature areas that need to be applied, which can separate target objects and identify backgrounds with similar feature parameters. At present, there are a large number of image segmentation technology research results. Among all these segmentation technologies, the threshold segmentation method is widely used because of its simple algorithm and wide application range, and most of the threshold segmentation methods select the best threshold through the gray histogram of the image. , such as en...

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Problems solved by technology

However, the 2D Otsu method sometimes cannot achieve good segmentation results, especially for remote sensing images that contain more noise. Therefore, this paper proposes a new algorithm for segmenting noisy remote sensing images

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