Prototype Contrastive Learning for Building Extraction From Remote Sensing Images

Chen, Zhenshuai; Xiang, Wei; Lin, Zhiyuan; Yu, Chuang; Liu, Yunpeng

doi:10.1109/lgrs.2023.3316641

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Compared with various remote sensing targets, buildings exhibit notable distinctions in terms of their significant scale variations, diverse geometries, intricate structures, and abundant internal intricacies [34]. Nonetheless, they frequently encounter challenges stemming from occlusion by surrounding objects or neighboring entities possessing similar textures and structures.…”

Section: Mafe Modulementioning

confidence: 99%

Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images

Chang,

He,

et al. 2024

Sensors

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The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, becoming integral to building extraction methodologies and yielding noteworthy performance outcomes. Nonetheless, prevailing DNN-based models for building extraction often overlook spatial information during the feature extraction phase. Additionally, many existing models employ a simplistic and direct approach in the feature fusion stage, potentially leading to spurious target detection and the amplification of internal noise. To address these concerns, we present a multi-scale attention network (MSANet) tailored for building extraction from high-resolution remote sensing images. In our approach, we initially extracted multi-scale building feature information, leveraging the multi-scale channel attention mechanism and multi-scale spatial attention mechanism. Subsequently, we employed adaptive hierarchical weighting processes on the extracted building features. Concurrently, we introduced a gating mechanism to facilitate the effective fusion of multi-scale features. The efficacy of the proposed MSANet was evaluated using the WHU aerial image dataset and the WHU satellite image dataset. The experimental results demonstrate compelling performance metrics, with the F1 scores registering at 93.76% and 77.64% on the WHU aerial imagery dataset and WHU satellite dataset II, respectively. Furthermore, the intersection over union (IoU) values stood at 88.25% and 63.46%, surpassing benchmarks set by DeepLabV3 and GSMC.

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Section: Mafe Modulementioning

confidence: 99%

Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images

Chang,

He,

et al. 2024

Sensors

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A method for extracting buildings from remote sensing images based on 3DJA-UNet3+

Li,

Zhu

et al. 2024

Sci Rep

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Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus .

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SANET: A Shape-Aware Building Footprints Extraction Method in Remote Sensing Images by Integrating Fourier Shape Descriptors

Hu,

Wu,

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Prototype Contrastive Learning for Building Extraction From Remote Sensing Images

Cited by 3 publications

References 19 publications

Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images

Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images

A method for extracting buildings from remote sensing images based on 3DJA-UNet3+

SANET: A Shape-Aware Building Footprints Extraction Method in Remote Sensing Images by Integrating Fourier Shape Descriptors

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