Remote sensing image classification method, image processing equipment and computer readable storage device

Abstract

The invention discloses a remote sensing image classification method, image processing equipment and a computer readable storage device. The remote sensing image classification method comprises the steps: performing first cutting on a to-be-processed remote sensing image to obtain a first image; obtaining a first classification sample generated after the user performs first classification on at least a part of the first image, and inputting the first classification sample and the first image into a first supervised classifier to obtain a first classification result; performing second cutting on the to-be-processed remote sensing image to obtain a first patch; obtaining a second classification sample according to the first classification result and the first patch, and inputting the secondclassification sample and the to-be-processed remote sensing image into a second supervised classifier to obtain a second classification result; and obtaining a final classification result of the to-be-processed remote sensing image according to the second classification result. Through the above mode, the method can effectively improve the precision of a land utilization classification result.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing image classification method, image processing equipment and a computer-readable storage device. Background technique [0002] In the actual engineering project of land use classification, in order to achieve high accuracy of classification results, improving the sample accuracy in supervised classification is currently a feasible means. High-quality samples will increase the accuracy of the final classification results. However, in the actual production process, since the selection of samples is a process of human-computer interaction, a large number of samples need to be selected for large-scale production, which will result in increased workload of human-computer interaction and low work efficiency. [0003] For large-scale classification, it is necessary to increase the number of samples and evenly distribute samples to meet the requir...

AI Technical Summary

Problems solved by technology

And this will increase the workload of human-computer interaction selection samples

In addition, the selection of sample results is often affected by factors such as image quality and human experience

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