New Network Based on Unet++ and Densenet for Building Extraction from High Resolution Satellite Imagery

Tong, Zhonggui; Li, Yuxia; Li, Yuzhen; Fan, Kebin; Yu, Shengping; He, Lei

doi:10.1109/igarss39084.2020.9324166

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…A lack of global semantic information can result in omissions in large-scale building predictions, and a paucity of local details might cause small buildings to be overlooked. Semantic segmentation algorithms using the U-net [25] encoder-decoder structure, which capitalizes on residual and dense connectivity, have yielded significant advances in high-resolution remote sensing building segmentation [26,27]. The U-net structure encodes by downsampling the image and employs compressed feature representation for decoder upsampling, aiming to restore image resolution and facilitate predictions.…”

Section: Segmentation Based On Deep Learning Methodsmentioning

confidence: 99%

Multi-Scale Feature Fusion Attention Network for Building Extraction in Remote Sensing Images

Liu,

Gu,

et al. 2024

Electronics

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The efficient semantic segmentation of buildings in high spatial resolution remote sensing images is a technical prerequisite for land resource management, high-precision mapping, construction planning and other applications. Current building extraction methods based on deep learning can obtain high-level abstract features of images. However, the extraction of some occluded buildings is inaccurate, and as the network deepens, small-volume buildings are lost and edges are blurred. Therefore, we introduce a multi-resolution attention combination network, which employs a multiscale channel and spatial attention module (MCAM) to adaptively capture key features and eliminate irrelevant information, which improves the accuracy of building extraction. In addition, we present a layered residual connectivity module (LRCM) to enhance the expression of information at different scales through multi-level feature fusion, significantly improving the understanding of context and the capturing of fine edge details. Extensive experiments were conducted on the WHU aerial image dataset and the Massachusetts building dataset. Compared with state-of-the-art semantic segmentation methods, this network achieves better building extraction results in remote sensing images, proving the effectiveness of the method.

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Section: Segmentation Based On Deep Learning Methodsmentioning

confidence: 99%

Multi-Scale Feature Fusion Attention Network for Building Extraction in Remote Sensing Images

Liu,

Gu,

et al. 2024

Electronics

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“…These networks are mainly based on Fully Convolutional Networks (FCNs) [54], SegNet [55], U-Net [56], and DeepLab. For example, CNNs based on ResNet or DenseNet backbone networks combined with Conditional Random Fields (CRFs) [57], the U-Net++ network reconstructed with DenseNet as a backbone network [58], and the SegNet network improved with the Gaussian algorithm and Image Pyramid [59] are all CNN-based building footprint extraction methods. CNN-based methods have dominated the field of building footprint extraction for several years due to their ability to learn and extract complex features from VHR images.…”

Section: Related Work 21 Building Footprint Extraction Methodsmentioning

confidence: 99%

Transformer-Based Semantic Segmentation for Extraction of Building Footprints from Very-High-Resolution Images

Song

Zhu

2023

Sensors

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Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images. Vision Transformer networks have shown significant improvements in performance compared to traditional convolutional neural networks (CNNs) in semantic segmentation. Vision Transformer networks have different architectures to CNNs. Image patches, linear embedding, and multi-head self-attention (MHSA) are several of the main hyperparameters. How we should configure them for the extraction of objects in VHR images and how they affect the accuracy of networks are topics that have not been sufficiently investigated. This article explores the role of vision Transformer networks in the extraction of building footprints from very-high-resolution (VHR) images. Transformer-based models with different hyperparameter values were designed and compared, and their impact on accuracy was analyzed. The results show that smaller image patches and higher-dimension embeddings result in better accuracy. In addition, the Transformer-based network is shown to be scalable and can be trained with general-scale graphics processing units (GPUs) with comparable model sizes and training times to convolutional neural networks while achieving higher accuracy. The study provides valuable insights into the potential of vision Transformer networks in object extraction using VHR images.

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“…Many remote sensing building extraction methods based on deep convolutional neural networks have been presented since the emergence and development of convolutional neural networks. Semantic segmentation algorithms based on the encoder-decoder structure of Unet frequently utilize the characteristics of residual connectivity and dense connectivity and have achieved numerous research achievements in the semantic segmentation of high-resolution remote sense buildings [28][29][30]. To enhance the feature representation capability of deep convolutional neural networks for remote sensing image segmentation, researchers commonly adopt the attention mechanism in the feature extraction stage to obtain more information about the target of remote sensing images and suppress the background, noise, and other interference feature of remote sensing images.…”

Section: Proposed Methodsmentioning

confidence: 99%

ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image

Deng

Meng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Aimed at the challenge of low accuracy of building segmentation caused by poor continuity of remote-sensing-image regions and blurred boundaries, a remote sensing building semantics segmentation algorithm based on multi-scale regional consistent attention supervision is proposed. Firstly, based on the Unet encoder-decoder architecture, the proposed algorithm constructs the region attention network (ReA-Net), which employs a multi-scale receptive field-guidance model to simultaneously focus on regional features and edge details of remote sensing image objects. Secondly, the self-attention mechanism is employed to establish the correlation representation of regional-level features of remote sensing images, and multi-scale regional attention features of remote sensing images are obtained through weighted regional-level correlation mapping. Finally, to address the lack of spatial correlation constraints on the prediction of remote sensing images segmentation, a loss function with multi-scale neighborhood consistency supervision is suggested to constrain the consistency of pixel label assignment related to a local region. Experimental results on WHU Building Dataset showed that Intersection over Union (IOU) reached 91.6%, precision reached 95.61%, recall reached 95.68% recall and F1-score reached 95.64%; On the Massachusetts building dataset, IOU reached 74.77% and precision reached 83.93%, recall reached 87.53% and F1-score reached 85.69%. Therefore, the proposed algorithm not only has a good segmentation effect but also has a strong robustness for remote sensing building image segmentation.

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New Network Based on Unet++ and Densenet for Building Extraction from High Resolution Satellite Imagery

Cited by 12 publications

References 6 publications

Multi-Scale Feature Fusion Attention Network for Building Extraction in Remote Sensing Images

Multi-Scale Feature Fusion Attention Network for Building Extraction in Remote Sensing Images

Transformer-Based Semantic Segmentation for Extraction of Building Footprints from Very-High-Resolution Images

ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image

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