scholarly journals BSIRNet: A Road Extraction Network with Bidirectional Spatial Information Reasoning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Hai Tan ◽  
Hao Xu ◽  
Jiguang Dai
Keyword(s):  
Spatial Information ◽  
Feature Fusion ◽  
Extraction Methods ◽  
Automatic Extraction ◽  
Processing Unit ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Public ◽  
Automatic Navigation ◽  
Neural Network Structure

Automatic extraction of road information from remote sensing images is widely used in many fields, such as urban planning and automatic navigation. However, due to interference from noise and occlusion, the existing road extraction methods can easily lead to road discontinuity. To solve this problem, a road extraction network with bidirectional spatial information reasoning (BSIRNet) is proposed, in which neighbourhood feature fusion is used to capture spatial context dependencies and expand the receptive field, and an information processing unit with a recurrent neural network structure is used to capture channel dependencies. BSIRNet enhances the connectivity of road information through spatial information reasoning. Using the public Massachusetts road dataset and Wuhan University road dataset, the superiority of the proposed method is verified by comparing its results with those of other models.

scholarly journals DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

2021 ◽  
Vol 10 (3) ◽  
pp. 125
Author(s):  
Junqing Huang ◽  
Liguo Weng ◽  
Bingyu Chen ◽  
Min Xia
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Information ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Dual Function ◽  
Context Information ◽  
Remote Sensing Images ◽  
Feature Aggregation ◽  
Image Context

Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%.

A Fast Algorithm for Road Recognition in Remote Sensing Image

2010 ◽  
Vol 108-111 ◽  
pp. 1344-1347
Author(s):  
Li Li Li ◽  
Yong Xin Liu
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Fast Algorithm ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Feature Integration ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Simulation Results

In general, the road extraction methods in remote sensing images mainly are edge detection, feature integration, and so on. A fast road recognition arithmetic is presented in this paper. First using adaptive binarization arithmetic, the path on remote sensing images is extracted. Then morphological method is used to process image. Finally, the extracted image superimposed with the original and get clear road. Simulation results shows that this algorithm is efficiency, the anti-noise ability is enhance, and more precision.

scholarly journals Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Shengfu Li ◽  
Cheng Liao ◽  
Yulin Ding ◽  
Han Hu ◽  
Yang Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Long Distance ◽  
Features Fusion ◽  
Multi Scale ◽  
Boundary Recognition ◽  
Benchmark Datasets

Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.

Road Extraction from GF-1 Remote Sensing Images Based on Dilated Convolution Residual Network with Multi-Scale Feature Fusion

2021 ◽  
Vol 58 (2) ◽  
pp. 0228001
Author(s):  
马天浩 Ma Tianhao ◽  
谭海 Tan Hai ◽  
李天琪 Li Tianqi ◽  
吴雅男 Wu Yanan ◽  
刘祺 Liu Qi
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution

scholarly journals Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model

2020 ◽  
Vol 12 (5) ◽  
pp. 894 ◽  
Author(s):  
Peng Liu ◽  
Yongming Wei ◽  
Qinjun Wang ◽  
Yu Chen ◽  
Jingjing Xie
Keyword(s):  
Spatial Information ◽  
Extraction Methods ◽  
Disaster Mitigation ◽  
New Method ◽  
Automatic Extraction ◽  
Training Samples ◽  
Low Degree ◽  
Extraction Algorithm ◽  
Survey Results ◽  
Hierarchical Features

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.

AF-Net: All-scale Feature Fusion Network for Road Extraction from Remote Sensing Images

2021 ◽  
Author(s):  
Shide Zou ◽  
Fengchao Xiong ◽  
Haonan Luo ◽  
Jianfeng Lu ◽  
Yuntao Qian
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Scale Feature

scholarly journals Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification

2020 ◽  
Vol 12 (21) ◽  
pp. 3547 ◽  
Author(s):  
Yuanyuan Ren ◽  
Xianfeng Zhang ◽  
Yongjian Ma ◽  
Qiyuan Yang ◽  
Chuanjian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Spatial Information ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Machine Learning Algorithms ◽  
Image Feature ◽  
Remote Sensing Images ◽  
Land Covers

Remote sensing image segmentation with samples imbalance is always one of the most important issues. Typically, a high-resolution remote sensing image has the characteristics of high spatial resolution and low spectral resolution, complex large-scale land covers, small class differences for some land covers, vague foreground, and imbalanced distribution of samples. However, traditional machine learning algorithms have limitations in deep image feature extraction and dealing with sample imbalance issue. In the paper, we proposed an improved full-convolution neural network, called DeepLab V3+, with loss function based solution of samples imbalance. In addition, we select Sentinel-2 remote sensing images covering the Yuli County, Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China as data sources, then a typical region image dataset is built by data augmentation. The experimental results show that the improved DeepLab V3+ model can not only utilize the spectral information of high-resolution remote sensing images, but also consider its rich spatial information. The classification accuracy of the proposed method on the test dataset reaches 97.97%. The mean Intersection-over-Union reaches 87.74%, and the Kappa coefficient 0.9587. The work provides methodological guidance to sample imbalance correction, and the established data resource can be a reference to further study in the future.

scholarly journals HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information

2019 ◽  
Vol 8 (12) ◽  
pp. 571 ◽  
Author(s):  
Yan Xie ◽  
Fang Miao ◽  
Kai Zhou ◽  
Jing Peng
Keyword(s):  
Semantic Information ◽  
Spatial Information ◽  
Feature Fusion ◽  
High Order ◽  
Local Context ◽  
Model Parameters ◽  
Road Extraction ◽  
Long Distance ◽  
Global Perception ◽  
Intermediate Output

Road extraction is a unique and difficult problem in the field of semantic segmentation because roads have attributes such as slenderness, long span, complexity, and topological connectivity, etc. Therefore, we propose a novel road extraction network, abbreviated HsgNet, based on high-order spatial information global perception network using bilinear pooling. HsgNet, taking the efficient LinkNet as its basic architecture, embeds a Middle Block between the Encoder and Decoder. The Middle Block learns to preserve global-context semantic information, long-distance spatial information and relationships, and different feature channels’ information and dependencies. It is different from other road segmentation methods which lose spatial information, such as those using dilated convolution and multiscale feature fusion to record local-context semantic information. The Middle Block consists of three important steps: (1) forming a feature resource pool to gather high-order global spatial information; (2) selecting a feature weight distribution, enabling each pixel position to obtain complementary features according to its own needs; and (3) inversely mapping the intermediate output feature encoding to the size of the input image by expanding the number of channels of the intermediate output feature. We compared multiple road extraction methods on two open datasets, SpaceNet and DeepGlobe. The results show that compared to the efficient road extraction model D-LinkNet, our model has fewer parameters and better performance: we achieved higher mean intersection over union (71.1%), and the model parameters were reduced in number by about 1/4.

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