scholarly journals Web-Net: A Novel Nest Networks with Ultra-Hierarchical Sampling for Building Extraction from Aerial Imageries

2019 ◽  
Vol 11 (16) ◽  
pp. 1897 ◽  
Author(s):  
Yan Zhang ◽  
Weiguo Gong ◽  
Jingxi Sun ◽  
Weihong Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Network Architecture ◽  
Structural Information ◽  
Rapid Development ◽  
Building Extraction ◽  
Feature Maps ◽  
Hierarchical Sampling ◽  
Nested Network ◽  
Local Cues

How to efficiently utilize vast amounts of easily accessed aerial imageries is a critical challenge for researchers with the proliferation of high-resolution remote sensing sensors and platforms. Recently, the rapid development of deep neural networks (DNN) has been a focus in remote sensing, and the networks have achieved remarkable progress in image classification and segmentation tasks. However, the current DNN models inevitably lose the local cues during the downsampling operation. Additionally, even with skip connections, the upsampling methods cannot properly recover the structural information, such as the edge intersections, parallelism, and symmetry. In this paper, we propose the Web-Net, which is a nested network architecture with hierarchical dense connections, to handle these issues. We design the Ultra-Hierarchical Sampling (UHS) block to absorb and fuse the inter-level feature maps to propagate the feature maps among different levels. The position-wise downsampling/upsampling methods in the UHS iteratively change the shape of the inputs while preserving the number of their parameters, so that the low-level local cues and high-level semantic cues are properly preserved. We verify the effectiveness of the proposed Web-Net in the Inria Aerial Dataset and WHU Dataset. The results of the proposed Web-Net achieve an overall accuracy of 96.97% and an IoU (Intersection over Union) of 80.10% on the Inria Aerial Dataset, which surpasses the state-of-the-art SegNet 1.8% and 9.96%, respectively; the results on the WHU Dataset also support the effectiveness of the proposed Web-Net. Additionally, benefitting from the nested network architecture and the UHS block, the extracted buildings on the prediction maps are obviously sharper and more accurately identified, and even the building areas that are covered by shadows can also be correctly extracted. The verified results indicate that the proposed Web-Net is both effective and efficient for building extraction from high-resolution remote sensing images.

scholarly journals DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery

2019 ◽  
Vol 11 (20) ◽  
pp. 2380 ◽  
Author(s):  
Liu ◽  
Luo ◽  
Huang ◽  
Hu ◽  
Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Spatial Information ◽  
Training Data ◽  
Fine Tuning ◽  
Building Extraction ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Remote Sensing Imagery

Deep convolutional neural networks have promoted significant progress in building extraction from high-resolution remote sensing imagery. Although most of such work focuses on modifying existing image segmentation networks in computer vision, we propose a new network in this paper, Deep Encoding Network (DE-Net), that is designed for the very problem based on many lately introduced techniques in image segmentation. Four modules are used to construct DE-Net: the inceptionstyle downsampling modules combining a striding convolution layer and a max-pooling layer, the encoding modules comprising six linear residual blocks with a scaled exponential linear unit (SELU) activation function, the compressing modules reducing the feature channels, and a densely upsampling module that enables the network to encode spatial information inside feature maps. Thus, DE-Net achieves stateoftheart performance on the WHU Building Dataset in recall, F1-Score, and intersection over union (IoU) metrics without pretraining. It also outperformed several segmentation networks in our self-built Suzhou Satellite Building Dataset. The experimental results validate the effectiveness of DE-Net on building extraction from aerial imagery and satellite imagery. It also suggests that given enough training data, designing and training a network from scratch may excel fine-tuning models pre-trained on datasets unrelated to building extraction.

scholarly journals Building Extraction in Very High Resolution Imagery by Dense-Attention Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1768 ◽  
Author(s):  
Hui Yang ◽  
Penghai Wu ◽  
Xuedong Yao ◽  
Yanlan Wu ◽  
Biao Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Building Extraction ◽  
Learning Networks ◽  
Feature Maps ◽  
Low Level ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
High Level ◽  
Very High

Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder–decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red–green–blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods.

Reducing the stride of the convolution kernel: a simple and effective strategy to increase the performance of CNN in building extraction from remote sensing image

2021 ◽  
Author(s):  
Meng Chen ◽  
Jianjun Wu ◽  
Feng Tian
Keyword(s):  
Remote Sensing ◽  
Emergency Response ◽  
Learning Technology ◽  
Convolution Kernel ◽  
Building Extraction ◽  
Effective Strategy ◽  
Feature Maps ◽  
Data Set ◽  
Irregular Shapes ◽  
Deep Layers

<p>Automatically extracting buildings from remote sensing images (RSI) plays important roles in urban planning, population estimation, disaster emergency response, etc. With the development of deep learning technology, convolutional neural networks (CNN) with better performance than traditional methods have been widely used in extracting buildings from remote sensing imagery (RSI). But it still faces some problems. First of all, low-level features extracted by shallow layers and abstract features extracted by deep layers of the artificial neural network could not be fully fused. it makes building extraction is often inaccurate, especially for buildings with complex structures, irregular shapes and small sizes. Secondly, there are so many parameters that need to be trained in a network, which occupies a lot of computing resources and consumes a lot of time in the training process. By analyzing the structure of the CNN, we found that abstract features extracted by deep layers with low geospatial resolution contain more semantic information. These abstract features are conducive to determine the category of pixels while not sensitive to the boundaries of the buildings. We found the stride of the convolution kernel and pooling operation reduced the geospatial resolution of feature maps, so, this paper proposed a simple and effective strategy—reduce the stride of convolution kernel contains in one of the layers and reduced the number of convolutional kernels to alleviate the above two bottlenecks. This strategy was used to deeplabv3+net and the experimental results for both the WHU Building Dataset and Massachusetts Building Dataset. Compared with the original deeplabv3+net the result showed that this strategy has a better performance. In terms of WHU building data set, the Intersection over Union (IoU) increased by 1.4% and F1 score increased by 0.9%; in terms of Massachusetts Building Dataset, IoU increased by 3.31% and F1 score increased by 2.3%.</p>

scholarly journals BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (6) ◽  
pp. 1050 ◽  
Author(s):  
Zhenfeng Shao ◽  
Penghao Tang ◽  
Zhongyuan Wang ◽  
Nayyer Saleem ◽  
Sarath Yam ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Input Image ◽  
Economic Forecast ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Global Features ◽  
Prediction Module ◽  
Deep Learning Network ◽  
The One

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.

scholarly journals Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

2020 ◽  
Vol 12 (2) ◽  
pp. 311 ◽  
Author(s):  
Chun Liu ◽  
Doudou Zeng ◽  
Hangbin Wu ◽  
Yin Wang ◽  
Shoujun Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Small Sample ◽  
Connectivity Pattern ◽  
Feature Maps ◽  
Convolutional Network ◽  
Urban Land Cover

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.

Building Extraction from High-Resolution Remote Sensing Images Based on GrabCut with Automatic Selection of Foreground and Background Samples

2020 ◽  
Vol 86 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Ka Zhang ◽  
Hui Chen ◽  
Wen Xiao ◽  
Yehua Sheng ◽  
Dong Su ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Geodesic Distance ◽  
Extraction Methods ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Canny Operator ◽  
Contour Lines ◽  
Average Accuracy ◽  
Seed Points

This article proposes a new building extraction method from high-resolution remote sensing images, based on GrabCut, which can automatically select foreground and background samples under the constraints of building elevation contour lines. First the image is rotated according to the direction of pixel displacement calculated by the rational function Model. Second, the Canny operator, combined with morphology and the Hough transform, is used to extract the building's elevation contour lines. Third, seed points and interesting points of the building are selected under the constraint of the contour line and the geodesic distance. Then foreground and background samples are obtained according to these points. Fourth, GrabCut and geometric features are used to carry out image segmentation and extract buildings. Finally, WorldView satellite images are used to verify the proposed method. Experimental results show that the average accuracy can reach 86.34%, which is 15.12% higher than other building extraction methods.

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