Remote Sensing Image Classification Method Based on Active Learning of Image Blocks

Abstract

The invention discloses a remote sensing image classification method based on image block active learning and belongs to the technical field of image information processing. The method comprises the following steps of remote sensing image blocking, initial sample selection, classifier model training, active learning sample selection, training sample set and classifier model updating, classification process iteration; image block classification prediction, and conversion of a block classification result into a pixel classification result. The remote sensing image classification method serves an image block as a research object, compared with a traditional remote sensing image classification method based on active learning of pixel points, under the same experiment condition, the classification result of the image is more accurate, a block sample screened out by the active learning can more rapidly and accurately conduct manual annotating, constitutive properties of the classified image are stronger, spots directly brought by classification of the pixel points are greatly reduced, and the better visual effect is brought to people.

Description

technical field [0001] The invention relates to the technical field of image information processing, in particular to a remote sensing image classification method based on active learning of image blocks. Background technique [0002] The improvement of spatial resolution and spectral resolution of satellite remote sensing system enables us to identify smaller objects from remote sensing images, such as residential buildings, commercial buildings, public transport systems and public utility equipment. A large amount of information mined from remote sensing images can be applied to fields such as disaster monitoring and assessment, urban and regional planning, and environmental monitoring. [0003] The classification of remote sensing images is a method of information extraction, which refers to the process of identifying ground objects based on the spectral characteristics, spatial characteristics, and temporal characteristics of the ground objects in the remote sensing imag...

AI Technical Summary

Problems solved by technology

[0010] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing remote sensing image classification method based on active learning, and provide a remote sensing image classification method based on image block active learning, which effectively improves the classification accuracy while reducing the burden of manual labeling. rate, and the visual effect of the final classification result map is better, and the phenomenon of "spots" is greatly reduced

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