Semantic Equalization Learning for Semi-Supervised SAR Building Segmentation

Lee, Eungbean; Jeong, Somi; Kim, Junhee; Sohn, Kwanghoon

doi:10.1109/lgrs.2022.3192568

Cited by 5 publications

(1 citation statement)

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“…A study [78] explores pseudo-labeling, where a model is initially trained using a small set of labeled data to create pseudo-segmentation maps for unlabeled samples. Consistency training-based approaches enforce prediction consistency by assigning diverse perturbations to the input [84] [85], which are more efficient to implement than the other methods. Domain adaptation is aimed at transferring knowledge from a source domain to a target domain, mitigating domain shift.…”

Section: A Building Footprint Generationmentioning

confidence: 99%

A Review of Building Extraction From Remote Sensing Imagery: Geometrical Structures and Semantic Attributes

Li,

Mou,

Sun

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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In the remote sensing community, extracting buildings from remote sensing imagery has triggered great interest. While many studies have been conducted, a comprehensive review of these approaches that are applied to optical and synthetic aperture radar (SAR) imagery is still lacking. Therefore, we provide an in-depth review of both early efforts and recent advances, which are aimed at extracting geometrical structures or semantic attributes of buildings, including building footprint generation, building facade segmentation, roof segment and superstructure segmentation, building height retrieval, building type classification, building change detection, and annotation data correction. Furthermore, a list of corresponding benchmark datasets is given. Finally, challenges and outlooks of existing approaches as well as promising applications are discussed to enhance comprehension within this realm of research.

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Section: A Building Footprint Generationmentioning

confidence: 99%