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High-resolution remote sensing image building contour extraction based on superpixel segmentation and LBP features
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.
High-resolution remote sensing image building contour extraction based on superpixel segmentation and LBP features
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.
High-resolution remote sensing image building contour extraction based on superpixel segmentation and LBP features
Mao, Feiyue (editor) / Wang, Chunmei (editor) / Yu, Zhaowu (editor) / Zuo, Zhongwei (author)
International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023) ; 2023 ; Kaifeng, China
Proc. SPIE ; 12815
2023-11-15
Conference paper
Electronic Resource
English
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