Boundary Shape-Preserving Model for Building Mapping From High-Resolution Remote Sensing Images

Hu, Anna; Wu, Liang; Chen, Siqiong; Xu, Yongyang; Wang, Haitao; Xie, Zixing

doi:10.1109/tgrs.2023.3277699

Cited by 9 publications

(4 citation statements)

References 48 publications

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“…Some works [52] [53] use semantic segmentation networks to learn building boundaries. To refine the boundaries of individual buildings, other works exploit instance segmentation networks (e.g., Mask R-CNN [54]) for building boundary learning [55] [56]. To obtain sharp building boundaries, some studies exploit the active contour model (ACM) where parameterizations are learned by an end-to-end network [57] [58].…”

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%

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

View full text Add to dashboard Cite

show abstract

“…Since they are not end-to-end trainable, building segmentation errors of the first stage will be accumulated throughout the following stags, resulting in sub-optimal performance and irregular buildings. To tackle these problems, some endto-end methods [20]- [23], [39] have emerged by integrating building segmentation, polygonization, and refinement into a HiT is a two-stage building mapping framework, which includes classification, bounding box regression, and polygon heads. The polygon head predicts serialized vertices of a building, together with building detection.…”

Section: A Post-processing Based Building Mappingmentioning

confidence: 99%

Multi-Layer Fault-Tolerant Protection Strategies for Hybrid Distribution Transformers Integrated Photovoltaic Systems

Zhang

Liu

Wang

et al. 2023

IEEE Trans. on Ind. Applicat.

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Deep learning-based methods have been extensively explored for automatic building mapping from high-resolution remote sensing images over recent years. While most building mapping models produce vector polygons of buildings for geographic and mapping systems, dominant methods typically decompose polygonal building extraction in some sub-problems, including segmentation, polygonization, and regularization, leading to complex inference procedures, low accuracy, and poor generalization. In this paper, we propose a simple and novel building mapping method with Hierarchical Transformers, called HiT, improving polygonal building mapping quality from high-resolution remote sensing images. HiT builds on a two-stage detection architecture by adding a polygon head parallel to classification and bounding box regression heads. HiT simultaneously outputs building bounding boxes and vector polygons, which is fully end-to-end trainable. The polygon head formulates a building polygon as serialized vertices with the bidirectional characteristic, a simple and elegant polygon representation avoiding the start or end vertex hypothesis. Under this new perspective, the polygon head adopts a transformer encoder-decoder architecture to predict serialized vertices supervised by the designed bidirectional polygon loss. Furthermore, a hierarchical attention mechanism combined with convolution operation is introduced in the encoder of the polygon head, providing more geometric structures of building polygons at vertex and edge levels. Comprehensive experiments on two benchmarks (the CrowdAI and Inria datasets) demonstrate that our method achieves a new state-of-the-art in terms of instance segmentation and polygonal metrics compared with state-of-theart methods. Moreover, qualitative results verify the superiority and effectiveness of our model under complex scenes.

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“…Convolutional neural networks (CNNs) have exceptionally performed in RS image segmentation [18][19][20][21][22][23]. Remarkably, the fully convolutional network (FCN) method [24] enables end-to-end training and pixel-level classification, thereby propelling the advancement of CNNs in image segmentation.…”

Section: Introductionmentioning

confidence: 99%

CNN and Transformer Fusion for Remote Sensing Image Semantic Segmentation

Chen,

Li,

Liu

et al. 2023

Remote Sensing

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Semantic segmentation of remote sensing images has been widely used in environmental protection, geological disaster discovery, and natural resource assessment. With the rapid development of deep learning, convolutional neural networks (CNNs) have dominated semantic segmentation, relying on their powerful local information extraction capabilities. Due to the locality of convolution operation, it can be challenging to obtain global context information directly. However, Transformer has excellent potential in global information modeling. This paper proposes a new hybrid convolutional and Transformer semantic segmentation model called CTFuse, which uses a multi-scale convolutional attention module in the convolutional part. CTFuse is a serial structure composed of a CNN and a Transformer. It first uses convolution to extract small-size target information and then uses Transformer to embed large-size ground target information. Subsequently, we propose a spatial and channel attention module in convolution to enhance the representation ability for global information and local features. In addition, we also propose a spatial and channel attention module in Transformer to improve the ability to capture detailed information. Finally, compared to other models used in the experiments, our CTFuse achieves state-of-the-art results on the International Society of Photogrammetry and Remote Sensing (ISPRS) Vaihingen and ISPRS Potsdam datasets.

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Boundary Shape-Preserving Model for Building Mapping From High-Resolution Remote Sensing Images

Cited by 9 publications

References 48 publications

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

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

Multi-Layer Fault-Tolerant Protection Strategies for Hybrid Distribution Transformers Integrated Photovoltaic Systems

CNN and Transformer Fusion for Remote Sensing Image Semantic Segmentation

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