Remote sensing image segmentation and identification method based on superpixel marking

Abstract

The invention provides a remote sensing image segmentation and identification method based on superpixel marking. Superpixel segmentation results are obtained by performing over-segmentation on remote sensing images by use of a superpixel segmentation algorithm, and learning samples are obtained by performing classification marking on superpixel blocks. Then, visual features of superpixel samples are extracted, these learning samples are trained by taking marking results as teacher signal classifiers, and trained classifier information is stored. The superpixel results are obtained by performing the over-segmentation on the remote sensing images to be analyzed, a visual feature of each superpixel is extracted and then is sent to the classifiers for classification, and after each superpixel block obtains a class mark, the superpixel blocks of the same class marks are merged, i.e., all areas of the images to be analyzed obtain class information. According to the invention, the remote sensing images are prevented from being directly segmented, edge information of actual areas is reserved to a quite large degree, segmentation and identification processes are integrated together, and the segmentation and identification capabilities are more excellent.

Description

technical field [0001] The invention relates to a method for segmenting and identifying remote sensing images. Background technique [0002] Remote sensing image is an intuitive carrier of comprehensive information on ground objects. With the development of remote sensing technology, it is an urgent and complicated problem to process remote sensing images and obtain various information from them. The analysis of remote sensing images plays an important role in various aspects such as geological exploration, agriculture and forestry. Segmentation and recognition of remote sensing images is an important research topic in the field of remote sensing digital image processing. Region segmentation and classification description can achieve the purpose of recognition, classification and interpretation of image information, which has very important military and civilian values. At present, there are many segmentation and recognition methods of remote sensing images, but there is st...

AI Technical Summary

Problems solved by technology

Overlapping division is an improved method based on fixed division. Although it can improve the accuracy to a certain extent, it still loses the boundary information of the actual area.

On the other hand, the current image segmentation methods and technologies cannot achieve a good segmentation effect on remote sensing images.

These factors limit the application of existing methods in remote sensing image segmentation and recognition

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