ATTENTION BASED CONVOLUTIONAL NEURAL NETWORK FOR BUILDING EXTRACTION FROM VERY HIGH RESOLUTION REMOTE SENSING IMAGE

Abstract. Buildings are a major element in the formation of cities and are essential for urban mapping. The precise extraction of buildings from remote sensing data has become a significant topic and has received much attention in recent years. The recently developed convolutional neural networks have shown effective and superior performance to perform well on learning high-level and discriminative features in extracting buildings because of the outstanding feature learning and end-to-end pixel labelling abilities. However, it is difficult to use the features of different levels with a certain degree of importance that is appropriate to deep learning networks. To tackle this problem, a network based on U-Nets and the attention mechanism block was proposed. The network contains an encoder part and a decoder part and a spatial attention module. The special architecture presented in this article enhances the propagation of features and effectively utilizes the features at various levels to reduce errors. The other remarkable thing is that attention module blocks only lead to a minimal increase in model complexity. We effectively demonstrate an improvement of building extraction accuracy on challenging Potsdam and Vaihingen benchmark datasets. The results of this paper show that the proposed architecture improves building extraction in very high resolution remote sensing images compared to previous models.

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

Remote Sensing ◽

10.3390/rs10111768 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1768 ◽

Cited By ~ 24

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.

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Validation of High-Density Airborne LiDAR-Based Feature Extraction Using Very High Resolution Optical Remote Sensing Data

Advances in Remote Sensing ◽

10.4236/ars.2013.24033 ◽

2013 ◽

Vol 02 (04) ◽

pp. 297-311 ◽

Cited By ~ 3

Author(s):

Shridhar D. Jawak ◽

Satej N. Panditrao ◽

Alvarinho J. Luis

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

High Resolution ◽

Remote Sensing Data ◽

High Density ◽

Airborne Lidar ◽

Optical Remote Sensing ◽

Sensing Data ◽

Very High

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CMGFNet: A deep cross-modal gated fusion network for building extraction from very high-resolution remote sensing images

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2021.12.007 ◽

2022 ◽

Vol 184 ◽

pp. 96-115

Author(s):

Hamidreza Hosseinpour ◽

Farhad Samadzadegan ◽

Farzaneh Dadrass Javan

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Building Extraction ◽

Remote Sensing Images ◽

Very High

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A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

Remote Sensing ◽

10.3390/rs11050482 ◽

2019 ◽

Vol 11 (5) ◽

pp. 482 ◽

Cited By ~ 6

Author(s):

Qi Bi ◽

Kun Qin ◽

Han Zhang ◽

Ye Zhang ◽

Zhili Li ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Common Knowledge ◽

Remote Sensing Image ◽

Morphological Operations ◽

Building Extraction ◽

Multi Scale ◽

Training Samples ◽

Image Building ◽

Very High

Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method.

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Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Remote Sensing ◽

10.3390/rs10010144 ◽

2018 ◽

Vol 10 (1) ◽

pp. 144 ◽

Cited By ~ 114

Author(s):

Yongyang Xu ◽

Liang Wu ◽

Zhong Xie ◽

Zhanlong Chen

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Building Extraction ◽

Remote Sensing Imagery ◽

Very High

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CGSANet: A contour-guided and local structure-aware encoder-decoder network for accurate building extraction from very high-resolution remote sensing imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3139017 ◽

2021 ◽

pp. 1-1

Author(s):

Shanxiong Chen ◽

Wenzhong Shi ◽

Mingting Zhou ◽

Min Zhang ◽

Zhaoxin Xuan

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Local Structure ◽

Building Extraction ◽

Remote Sensing Imagery ◽

Very High

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Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network

Remote Sensing ◽

10.3390/rs11242970 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2970 ◽

Cited By ~ 3

Author(s):

Ziran Ye ◽

Yongyong Fu ◽

Muye Gan ◽

Jinsong Deng ◽

Alexis Comber ◽

...

Keyword(s):

Neural Network ◽

High Resolution ◽

Spatial Information ◽

Remote Sensing Data ◽

Aerial Imagery ◽

Building Extraction ◽

Feature Maps ◽

Convolutional Network ◽

Wide Range ◽

Very High

Automated methods to extract buildings from very high resolution (VHR) remote sensing data have many applications in a wide range of fields. Many convolutional neural network (CNN) based methods have been proposed and have achieved significant advances in the building extraction task. In order to refine predictions, a lot of recent approaches fuse features from earlier layers of CNNs to introduce abundant spatial information, which is known as skip connection. However, this strategy of reusing earlier features directly without processing could reduce the performance of the network. To address this problem, we propose a novel fully convolutional network (FCN) that adopts attention based re-weighting to extract buildings from aerial imagery. Specifically, we consider the semantic gap between features from different stages and leverage the attention mechanism to bridge the gap prior to the fusion of features. The inferred attention weights along spatial and channel-wise dimensions make the low level feature maps adaptive to high level feature maps in a target-oriented manner. Experimental results on three publicly available aerial imagery datasets show that the proposed model (RFA-UNet) achieves comparable and improved performance compared to other state-of-the-art models for building extraction.

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