Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss

The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.

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Multiscale Road Extraction in Remote Sensing Images

Computational Intelligence and Neuroscience ◽

10.1155/2019/2373798 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Aziguli Wulamu ◽

Zuxian Shi ◽

Dezheng Zhang ◽

Zheyu He

Keyword(s):

Remote Sensing ◽

Network Architecture ◽

Semantic Segmentation ◽

Road Extraction ◽

Remote Sensing Images ◽

The Road ◽

Proposed Model ◽

Different Types ◽

Spatial Pyramid Pooling ◽

The One

Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.

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Remote Sensing Road Extraction by Road Segmentation Network

Applied Sciences ◽

10.3390/app11115050 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5050

Author(s):

Jiahai Tan ◽

Ming Gao ◽

Kai Yang ◽

Tao Duan

Keyword(s):

Remote Sensing ◽

Attention Mechanism ◽

Context Information ◽

Road Extraction ◽

Remote Sensing Images ◽

Long Distance ◽

The Road ◽

Road Segmentation ◽

Context Characteristics

Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, trees, and shadows. The objective of this paper is to enhance context and strip features of the road by designing UNet-like architecture. The overall method first enhances the context characteristics in the segmentation step and then maintains the stripe characteristics in a refinement step. The segmentation step exploits an attention mechanism to enhance the context information between the adjacent layers. To obtain the strip features of the road, the refinement step introduces the strip pooling in a refinement network to restore the long distance dependent information of the road. Extensive comparative experiments demonstrate that the proposed method outperforms other methods, achieving an overall accuracy of 98.25% on the DeepGlobe dataset, and 97.68% on the Massachusetts dataset.

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Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

Remote Sensing ◽

10.3390/rs13132524 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2524

Author(s):

Ziyi Chen ◽

Dilong Li ◽

Wentao Fan ◽

Haiyan Guan ◽

Cheng Wang ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Semantic Segmentation ◽

Extraction Methods ◽

The Self ◽

Optical Remote Sensing ◽

Building Extraction ◽

Learning Models ◽

Remote Sensing Images ◽

Segmentation Methods

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

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Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet

Remote Sensing ◽

10.3390/rs11010047 ◽

2018 ◽

Vol 11 (1) ◽

pp. 47 ◽

Cited By ~ 3

Author(s):

Nan Wang ◽

Bo Li ◽

Qizhi Xu ◽

Yonghua Wang

Keyword(s):

Remote Sensing ◽

Anomaly Detection ◽

Superior Performance ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Low Contrast ◽

Ship Detection ◽

Wide Range ◽

Spatial Pyramid Pooling ◽

Spatial Pyramid

Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.

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A Fast Algorithm for Road Recognition in Remote Sensing Image

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.108-111.1344 ◽

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.

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Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi11010009 ◽

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.

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Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

Remote Sensing ◽

10.3390/rs11212499 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2499 ◽

Cited By ~ 6

Author(s):

Jiang Xin ◽

Xinchang Zhang ◽

Zhiqiang Zhang ◽

Wu Fang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Intelligent Transportation Systems ◽

Road Network ◽

Semantic Segmentation ◽

Transportation Systems ◽

Machine Learning Algorithms ◽

Road Extraction ◽

Remote Sensing Images ◽

Road Network Extraction

Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.

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Bridge Extraction Algorithm Based on Deep Learning and High-Resolution Satellite Image

Scientific Programming ◽

10.1155/2021/9961963 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Wenbing Yang ◽

Xiaoqi Gao ◽

Chunlei Zhang ◽

Feng Tong ◽

Guantian Chen ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Satellite Image ◽

Structural Similarity ◽

Remote Sensing Images ◽

The Road ◽

Extraction Algorithm ◽

Length Width ◽

On The Road

This paper proposes a novel method of extracting roads and bridges from high-resolution remote sensing images based on deep learning. Edge detection is performed on the images in the road area along with the road skeleton line, and the result of the detected binary edge is vectorized. The interference of protective belts on both sides of the road, road vehicles, road green belts, traffic signs, etc. and the shadow interference of the bridge itself are eliminated to determine the parallel sides of the road. The bridge features on the road are used to locate the detected bridge and obtain information such as the location, length, width, and direction of the bridge, verifying the experimental results of the Shaoguan Le point images. In addition, in order to learn higher-level road feature information, the algorithm in this paper introduces the hollow convolution and multicore pooling modules. Secondly, the residual refinement network further refines the output of the prediction network to improve the ambiguity of the prediction network results. In addition, in view of the small proportion of road pixels in remote sensing images, the network also integrates binary cross entropy, structural similarity, and intersection ratio loss function to reduce road information loss. The applicability of the proposed study was tested, and the results show that the algorithm is very effective for the extraction of road and bridge targets.

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Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields

Remote Sensing ◽

10.3390/rs13030465 ◽

2021 ◽

Vol 13 (3) ◽

pp. 465

Author(s):

Shuyang Wang ◽

Xiaodong Mu ◽

Dongfang Yang ◽

Hao He ◽

Peng Zhao

Keyword(s):

Remote Sensing ◽

Random Fields ◽

Conditional Random Fields ◽

Road Extraction ◽

Remote Sensing Images ◽

Feature Maps ◽

Convolutional Network ◽

The Road ◽

Disaster Assessment

Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task due to complex circumstances and factors such as occlusion. To improve the accuracy and connectivity of road extraction, we propose an inner convolution integrated encoder-decoder network with the post-processing of directional conditional random fields. Firstly, we design an inner convolutional network which can propagate information slice-by-slice within feature maps, thus enhancing the learning of road topology and linear features. Additionally, we present the directional conditional random fields to improve the quality of the extracted road by adding the direction of roads to the energy function of the conditional random fields. The experimental results on the Massachusetts road dataset show that the proposed approach achieves high-quality segmentation results, with the F1-score of 84.6%, which outperforms other comparable “state-of-the-art” approaches. The visualization results prove that the proposed approach is able to effectively extract roads from remote sensing images and can solve the road connectivity problem produced by occlusions to some extent.

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FuNet: A Novel Road Extraction Network with Fusion of Location Data and Remote Sensing Imagery

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010039 ◽

2021 ◽

Vol 10 (1) ◽

pp. 39

Author(s):

Kai Zhou ◽

Yan Xie ◽

Zhan Gao ◽

Fang Miao ◽

Lei Zhang

Keyword(s):

Remote Sensing ◽

Semantic Segmentation ◽

Histogram Equalization ◽

Extraction Methods ◽

Road Extraction ◽

Remote Sensing Imagery ◽

The Road ◽

Location Data ◽

Topological Connectivity

Road semantic segmentation is unique and difficult. Road extraction from remote sensing imagery often produce fragmented road segments leading to road network disconnection due to the occlusion of trees, buildings, shadows, cloud, etc. In this paper, we propose a novel fusion network (FuNet) with fusion of remote sensing imagery and location data, which plays an important role of location data in road connectivity reasoning. A universal iteration reinforcement (IteR) module is embedded into FuNet to enhance the ability of network learning. We designed the IteR formula to repeatedly integrate original information and prediction information and designed the reinforcement loss function to control the accuracy of road prediction output. Another contribution of this paper is the use of histogram equalization data pre-processing to enhance image contrast and improve the accuracy by nearly 1%. We take the excellent D-LinkNet as the backbone network, designing experiments based on the open dataset. The experiment result shows that our method improves over the compared advanced road extraction methods, which not only increases the accuracy of road extraction, but also improves the road topological connectivity.

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