Building Extraction Based on U-Net with an Attention Block and Multiple Losses

Guo, Mingqiang; Liu, Heng; Xu, Yongyang; Huang, Ying

doi:10.3390/rs12091400

Cited by 122 publications

(66 citation statements)

References 43 publications

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“…With the development of DCNNs in recent years, many algorithms have been proposed for processing remote sensing images [25][26][27][28][29][30][31][32]. The fully convolutional network [33] (FCN) replaces the fully connected layers with convolutional layers, making it possible for large-scale dense prediction.…”

Section: Related Workmentioning

confidence: 99%

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Liao

Han

et al. 2021

Remote Sensing

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Most of the existing approaches to the extraction of buildings from high-resolution orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise mask for buildings and trains end-to-end with manually labeled building maps. However, as buildings are highly structured, such a strategy suffers several problems, such as blurred boundaries and the adhesion to close objects. To alleviate the above problems, we proposed a new strategy that also considers the contours of the buildings. Both the contours and structures of the buildings are jointly learned in the same network. The contours are learnable because the boundary of the mask labels of buildings implicitly represents the contours of buildings. We utilized the building contour information embedded in the labels to optimize the representation of building boundaries, then combined the contour information with multi-scale semantic features to enhance the robustness to image spatial resolution. The experimental results showed that the proposed method achieved 91.64%, 81.34%, and 74.51% intersection over union (IoU) on the WHU, Aerial, and Massachusetts building datasets, and outperformed the state-of-the-art (SOTA) methods. It significantly improved the accuracy of building boundaries, especially for the edges of adjacent buildings. The code is made publicly available.

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Section: Related Workmentioning

confidence: 99%

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Liao

Han

et al. 2021

Remote Sensing

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“…Expanding from the lightweight dense network [ 22 ], Liu et al [ 23 ] proposed a relation-enhanced multi-scale convolutional network for land cover classification in urban areas. On the basis of the U-net [ 16 ] framework, Guo et al [ 24 ] utilized the attention module to improve the accuracy of building extraction by suppressing the background influence of irrelevant feature regions. Moreover, Cao et al [ 25 ] combined a feature extraction network (Resnet), semantic segmentation network (U-net), and integrated conditional random field for post-processing to achieve tree species classification.…”

Section: Related Workmentioning

confidence: 99%

Coastal Land Cover Classification of High-Resolution Remote Sensing Images Using Attention-Driven Context Encoding Network

Chen

Wang

et al. 2020

Sensors

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Low inter-class variance and complex spatial details exist in ground objects of the coastal zone, which leads to a challenging task for coastal land cover classification (CLCC) from high-resolution remote sensing images. Recently, fully convolutional neural networks have been widely used in CLCC. However, the inherent structure of the convolutional operator limits the receptive field, resulting in capturing the local context. Additionally, complex decoders bring additional information redundancy and computational burden. Therefore, this paper proposes a novel attention-driven context encoding network to solve these problems. Among them, lightweight global feature attention modules are employed to aggregate multi-scale spatial details in the decoding stage. Meanwhile, position and channel attention modules with long-range dependencies are embedded to enhance feature representations of specific categories by capturing the multi-dimensional global context. Additionally, multiple objective functions are introduced to supervise and optimize feature information at specific scales. We apply the proposed method in CLCC tasks of two study areas and compare it with other state-of-the-art approaches. Experimental results indicate that the proposed method achieves the optimal performances in encoding long-range context and recognizing spatial details and obtains the optimum representations in evaluation indexes.

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“…Semantic segmentation has been developed to automatically and intelligently extract objects from images, which is treated as object extraction from images at the pixel level. With the latest development in convolutional neural networks (CNN) [15,16], the performance of semantic segmentation has been significantly improved [17][18][19] because deep CNN extracts image feature information through downsampling. Accordingly, a large number of small objects are difficult to classify.…”

Section: Introductionmentioning

confidence: 99%

ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

Guo

et al. 2021

Remote Sensing

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Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 × 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.

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Building Extraction Based on U-Net with an Attention Block and Multiple Losses

Cited by 122 publications

References 43 publications

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Coastal Land Cover Classification of High-Resolution Remote Sensing Images Using Attention-Driven Context Encoding Network

ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

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