High-resolution remote sensing image building contour extraction based on superpixel segmentation and LBP features

Zhongwei Zuo

doi:10.1117/12.3010321

15 November 2023 High-resolution remote sensing image building contour extraction based on superpixel segmentation and LBP features

Zhongwei Zuo

Author Affiliations +

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128150H (2023) https://doi.org/10.1117/12.3010321
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

The traditional pixel-level extraction of building outlines is limited by the single feature, while the fitting of building outlines is limited by the problem of building corner extraction and complex shapes. This paper proposes a multi-level building contour extraction method that combines superpixel segmentation with LBP features. In this method, the SLIC method is used to segment the image, and the object-oriented method combined with LBP features is used to classify the building area for each segmented area, and then the building area is extracted using the pixel-level method combining LBP value and spectral feature. Building silhouettes. Experimental results show that this method is superior to traditional pixel-level extraction in terms of classification effect and accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhongwei Zuo "High-resolution remote sensing image building contour extraction based on superpixel segmentation and LBP features", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128150H (15 November 2023); https://doi.org/10.1117/12.3010321

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KEYWORDS

Image segmentation

Contour extraction

Feature extraction

Remote sensing

Image classification

Classification systems

Image fusion

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