A method for road feature recognition and related apparatus

By using a combination of target base classification models and ensemble classification models in multispectral remote sensing images, the accuracy and cost issues of road feature identification in remote sensing images are solved, achieving high-precision road feature identification and expanding industry applications.

CN115100524BActive Publication Date: 2026-05-15TENCENT TECHNOLOGY (SHENZHEN) CO LTD
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Patent Information

Application Number
CN202210761154.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-06-30
Publication Date
2026-05-15
Estimated Expiration
2042-06-30

AI Technical Summary

Technical Problem

Existing technologies for road feature identification in remote sensing images, especially hyperspectral remote sensing images, suffer from low identification accuracy, high computational overhead, and difficulty in being applied to multispectral remote sensing images, thus limiting the application of remote sensing images in the industry.

Method used

A multispectral remote sensing image recognition method is adopted, which preprocesses and predicts images using N target base classification models, and combines them with a target ensemble classification model to improve recognition accuracy and reduce costs.

Benefits of technology

High-precision road feature identification has been achieved in multispectral remote sensing images, expanding the application scenarios of the remote sensing image industry and reducing application costs.

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Abstract

Embodiments of the present application disclose a road feature recognition method and related device, which are used in the recognition scene of road features in multispectral remote sensing images, facilitate the application landing of the remote sensing image industry, reduce the application cost, and greatly improve the classification accuracy in the recognition process. The method can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation, and auxiliary driving. The method comprises: acquiring a multispectral remote sensing image to be recognized; preprocessing the multispectral remote sensing image to be recognized to obtain a first image; taking the first image as the input of N target base classification models to obtain N first classification results predicted by the N target base classification models for the first image; taking the N first classification results as the input of a target integrated classification model to obtain a target classification result, the target classification result being used to indicate the classification of road features in the multispectral remote sensing image to be recognized.
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