Road Extraction Method Research Articles

Forest roads extraction through a convolution neural network aided method

ABSTRACT Forest roads are important features of the environment that have significant impacts on wildlife habitats. The U-net and U-shaped Fully Convoluted Network (FCN) deep-learning methods have demonstrated great potential in successfully extracting paved roads in urban environments; however, they require a large amount of pixel-based training samples, which is resource-consuming. During this study, a convolutional neural network (CNN) aided method for forest road identification and extraction was developed. The algorithm utilized the multivariate Gaussian and Laplacian of Gaussian (LoG) filters and VGG 16 on high spatial resolution multispectral imagery to extract both primary road and secondary roads in forested areas. It was tested on imagery over two areas in the Hearst forest located in central Ontario, Canada. Based on validation against manually digitized roads, over 74% of the roads from both test areas were successfully extracted.

Road extraction method from remote sensing images based on improved U-Net network

Road extraction method from remote sensing images based on improved U-Net network

Road Extraction Using a Dual Attention Dilated-LinkNet Based on Satellite Images and Floating Vehicle Trajectory Data

Automatic extraction of road from multi-source remote sensing data has always been a challenging task. Factors such as shadow occlusion and multi-source data alignment errors prevent current deep learning-based road extraction methods from acquiring road features with high complementarity, redundancy, and crossover. Unlike previous works that capture contexts by multi-scale feature fusion, we propose a dual attention dilated-LinkNet (DAD-LinkNet) to adaptively integrate local road features with their global dependencies by joint using satellite image and floating vehicle trajectory data. Firstly, a joint least-squares feature matching-based floating vehicle trajectory correction model is used to correct the floating vehicle trajectory; then a convolutional network model DAD-LinkNet based on a dual-attention mechanism is proposed, and road features are extracted from the channel domain and spatial domain of the target image in turn by constructing a dual-attention module in the dilated convolutional layer and adopting a cascade connection; a weighted hyperparameter loss function is used as the loss function of the model; finally, the road extraction is completed based on the proposed DAD-LinkNet model. Experiments on three datasets show that the proposed DAD-LinkNet model outperforms the state-of-the-art methods in terms of accuracy and connectivity.

基于改进Encoder-Decoder网络的遥感影像道路提取

基于改进Encoder-Decoder网络的遥感影像道路提取

Road Extraction From High Spatial Resolution Remote Sensing Image Based on Multi-Task Key Point Constraints

To solve some problems of high spatial resolution remote sensing images caused by land coverage, building coverage and shading of trees, such as difficult road extraction and low precision, a road extraction method based on multi-task key point constraints is put forward in this article based on Linknet. At the preprocessing stage, an auxiliary constraint task is designed to solve the connectivity problem caused by shading during road extraction from remote sensing images. At the encoding & decoding stage, first, a position attention (PA) mechanism module and channel attention (CA) mechanism module are applied to realize the effective fusion of semantic information in the context during road extraction. Second, a multi-branch cascade dilated spatial pyramid (CDSP) is established with dilated convolution, by which the problem of loss of partial information during information extraction from remote sensing road image is solved and the detection accuracy is further improved. The method put forward in this article is verified through the experiment with public datasets and private datasets, revealing that the proposed method provides better performance than several state of the art techniques in terms of detection accuracy, recall, precision, and F1-score.

Road Extraction Method of Open-Pit Mine Based on Improved DeepLabv3+ Network

Road Extraction Method of Open-Pit Mine Based on Improved DeepLabv3+ Network

Automatic Road Extraction from High-Resolution Remote Sensing Images Using a Method Based on Densely Connected Spatial Feature-Enhanced Pyramid

Road extraction is an important task in remote sensing image information extraction. Recently, deep learning semantic segmentation has become an important method of road extraction. Due to the impact of the loss of multiscale spatial features, the results of road extraction still contain incomplete or fractured results. In this article, we proposed a deep learning model, which is called the dense-global-residual network that reduces the loss of spatial information and enhances context awareness. In the dense-global-residual network, the residual network is used to extract the features at different levels. To obtain more abundant multiscale features, a dense and global spatial pyramid pooling module based on Atrous Spatial Pyramid Pooling is built to perceive and aggregate the contextual information. The proposed method obtains better results on the GF-2 road dataset and public Massachusetts road dataset of aerial imagery. In order to prove the effectiveness of our method, we compared with four methods, such as DeepLabV3+, U-net, D-LinkNet, and coord-dense-global model, and found that the accuracy of our method is considerably better. Moreover, the dense-global-residual network can also effectively extract roads, especially trees and building shadows that occlude the road. In addition, our method can successfully extract roads in regions of different development levels in universality experiments. This indicates that the proposed method can effectively maintain the completeness and continuity of roads and improve the accuracy of road segmentation from high-resolution remote sensing images.

Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network

Two of the major applications in geospatial information system (GIS) and remote sensing fields are object detection and man-made feature extraction (e.g., road sections) from high-resolution remote sensing imagery. Extracting roads from high-resolution remotely sensed imagery plays a crucial role in multiple applications, such as navigation, emergency tasks, land cover change detection, and updating GIS maps. This study presents a deep learning technique based on a convolutional neural network (CNN) to classify and extract roads from orthophoto images. We applied the model on five orthophoto images to specify the superiority of the method for road extraction. First, we used principal component analysis and object-based image analysis for pre-processing to not only obtain spectral information but also add spatial and textural information for enhancing the classification accuracy. Then, the obtained results from the previous step were used as input for the CNN model to classify the images into road and non-road parts and trivial opening and closing operation are applied to extract connected road components from the images and remove holes inside the road parts. For the accuracy assessment of the proposed method, we used measurement factors such as precision, recall, F1 score, overall accuracy, and IOU. Achieved results showed that the average percentages of these factors were 91.09%, 95.32%, 93.15%, 94.44%, and 87.21%. The results were also compared with those of other existing methods. The comparison ascertained the reliability and superior performance of the suggested model architecture for extracting road regions from orthophoto images.

Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network

Road detection from the satellite images can be considered as a classification process in which pixels are divided into the road and non-road classes. In this research, an automatic road extraction using an artificial neural network (ANN) based on automatic information extraction from satellite images and self-adjusting of the hidden layer proposed. Parameters of non-urban road networks from satellite images using a histogram-based binary image segmentation technique are also presented. The segmentation method is implemented by determining a global threshold, which is obtained from a statistical analysis of a number of sample satellite images and their ground truths. The thresholding method is based on two major facts: first, the points corresponding to non-asphalt roads are brighter than other areas in non-urban images. Second, it is observed that in an aerial image, the area covered by roads is only a small fraction of total pixels. It is also observed that pixels corresponding to roads are generally populated at the very bright end of the image greyscale histogram. In this method, at first, the possible road pixels are selected by the proposed segmentation method. Then different parameters, including color, gradient, and entropy, are computed for each pixel from the source image. Finally, these features are used for the artificial neural network input. The results show that the accuracy of the proposed road extraction method is around 80%.

BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery

Automatic road extraction from high-resolution remote sensing imagery has various applications like urban planning and automatic navigation. Existing methods for automatic road extraction however, focus on regional accuracy but not on the boundary quality; and most of these road extraction methods yield discontinuous results due to noise and occlusions. To address these two problems, a Boundary and Topological-aware Road extraction Network (BT-RoadNet) is proposed. BT-RoadNet is a coarse-to-fine architecture composed of two encoder-to-decoder networks, a Coarse Map Predicting Module (CMPM) and Fine Map Predicting Module (FMPM). The CMPM learns to predict coarse road segmentation maps, in which a Spatial Context Module (SCM) is employed as a bridge to solve discontinuous problems. The FMPM is used to refine the coarse road maps by learning the difference between the coarse road extraction result and the ground truth. Experiments were conducted on the open Massachusetts Road Dataset, a newly annotated Wuhan University (WHU) Road Dataset, and three large satellite images. Quantitative and qualitative analysis demonstrate that the proposed BT-RoadNet can enhance road network extraction to deal with interruptions caused by shadows and occlusions, extract roads with different scales and materials, and handle roads under construction that have incomplete spectral and geometric properties.

Learning From GPS Trajectories of Floating Car for CNN-Based Urban Road Extraction With High-Resolution Satellite Imagery

Deep learning has achieved great success in recent years, among which the convolutional neural network (CNN) method is outstanding in image segmentation and image recognition. It is also widely used in satellite imagery road extraction and, generally, can obtain accurate and extraction results. However, at present, the extraction of roads based on CNN still requires a lot of manual preparation work, and a large number of samples can be marked to achieve extraction, which has to take long drawing cycle and high production cost. In this article, a new CNN sample set production method is proposed, which uses the GPS trajectories of floating car as training set (GPSTasST), for the multilevel urban roads extraction from high-resolution remote sensing imagery. This method rasterizes the GPS trajectories of floating car into a raster map and uses the processed raster map to label the satellite image to obtain a road extraction sample set. CNN can extract roads from remote sensing imagery by learning the training set. The results show that the method achieves a harmonic mean of precision and recall higher than road extraction method from single data source while eliminating the manual labeling work, which shows the effectiveness of this work.

A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

Feature-representation-transfer based road extraction method for cross-domain aerial images

Feature-representation-transfer based road extraction method for cross-domain aerial images

Gated Auxiliary Edge Detection Task for Road Extraction With Weight-Balanced Loss

Automated road extraction from very high-resolution (VHR) remote sensing imagery is important in many practical applications and has a long research history. Due to the diversity, narrowness, and sparsity of the road nature, extracting a full detailed road network remains a challenge, especially in the presence of interference. When applying semantic segmentation to deal with road extraction, U-Net-based architectures have achieved great progress through the use of dilated convolution or residual structure. However, the existing methods rarely focus on shape completeness and road continuity, and in fact, these are essential for road extraction. Inspirit by the multitask learning, in this letter, we present a novel road extraction architecture called gated auxiliary edge (GAE)-LinkNet with semantic segmentation as the main task and edge detection as the auxiliary task. With the proposed GatedBlocks, redundant features are filtered out and shape-relevant features stand out. Through the task loss weighing mechanism, these two tasks can work together seamlessly to make better use of the shape features. Experiments on a public road data set show that the proposed method is superior to state-of-the-art road extraction methods.

Feature transfer based adversarial domain adaptation method for cross-domain road extraction

In order to improve the cross-domain applicability of road segmentation, a feature transfer based adversarial domain adaptation method is presented for cross-domain road extraction. The presented method consists of two main parts, a feature transfer network and an adversarial domain adaption network. The feature transfer network transforms the images of the source domain into the feature space of the target domain. The adversarial domain adaption network learns to distinguish whether the results come from source domain or target domain, to promote the backbone network of road extraction to learn the common features of the road. The experimental results showed that the proposed method improved the generalization ability of the road extraction network and could extract the road target from cross-domain images accurately and effectively. The proposed method can realize the road extraction across domain without any annotation of target domain, so it has good application value.

An Improved Method for Road Extraction from High-Resolution Remote-Sensing Images that Enhances Boundary Information.

At present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the coord-dense-global (CDG) model, built on three parts: a coordconv module by putting coordinate information into feature maps aimed at reducing the loss of spatial information and strengthening road boundaries, an improved dense convolutional network (DenseNet) that could make full use of multiple features through own dense blocks, and a global attention module designed to highlight high-level information and improve category classification by using pooling operation to introduce global information. When tested on a complex road dataset from Massachusetts, USA, CDG achieved clearly superior performance to contemporary networks such as DeepLabV3+, U-net, and D-LinkNet. For example, its mean IoU (intersection of the prediction and ground truth regions over their union) and mean F1 score (evaluation metric for the harmonic mean of the precision and recall metrics) were 61.90% and 76.10%, respectively, which were 1.19% and 0.95% higher than the results of D-LinkNet (the winner of a road-extraction contest). In addition, CDG was also superior to the other three models in solving the problem of tree occlusion. Finally, in universality research with the Gaofen-2 satellite dataset, the CDG model also performed well at extracting the road network in the test maps of Hefei and Tianjin, China.

A ROAD EXTRACTION METHOD BASED ON HIGH RESOLUTION REMOTE SENSING IMAGE

Abstract. Aiming at the road extraction in high-resolution remote sensing images, the stroke width transformation algorithm is greatly affected by surrounding objects, and it is impossible to directly obtain high-precision road information. A new road extraction method combining stroke width transformation and mean drift is proposed. In order to reduce road holes and discontinuities, and preserve better edge information, the algorithm first performs denoising preprocessing by means of median filtering to the pre-processed image. Then, the mean shift algorithm is used for image segmentation. The adjacent parts of the image with similar texture and spectrum are treated as the same class, and then the fine areas less than the maximum stroke width are reduced. On the basis , the road information is extracted by the stroke width transformation algorithm, and the information also contains a small amount of interference information such as spots (non-road). In order to further improve road extraction accuracy and reduce speckle and non-road area interference, the basic operations and combinations in mathematical morphology are used to optimize it. The experimental results show that the proposed algorithm can accurately extract the roads on high-resolution remote sensing images, and the better the road features, the better the extraction effect. However, the applicability of the algorithm is greatly affected by the surrounding objects.

Semiautomatic Road Extraction Framework Based on Shape Features and LS-SVM from High-Resolution Images

Road network delineation from remote sensing imagery is useful in many applications related to humans in present days as it provides meaningful information which is very helpful in the field of transportation and other support systems of human civilization. However, the road network extraction from remote sensing images is a challenging problem because of various properties of road network altered by different factors. In this paper, a supervised multistage framework based on least square support vector machine (LS-SVM), mathematical morphology and road shape features is proposed to extract road networks from remote sensing images. In the first stage, after denoising the image using the nonlinear filter, the image is segmented using LS-SVM into two classes’ road or non-road but some non-road components like building structures, shadows of trees and other roads like objects are also classified as the road network. In the second stage, the morphology and primary shape features are used to remove the non-road objects. Finally, road centerlines are extracted using a method based on Euclidean distance transformation. The experimental results reflect the accuracy of the proposed work as compared to other road extraction methods.

Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

Road extraction from high-resolution remote sensing images is a challenging but hot research topic in the past decades. A large number of methods are invented to deal with this problem. This article provides a comprehensive review of these existing approaches. We classified the methods into heuristic and data-driven. The heuristic methods are the mainstream in the early years, and the data-driven methods based on deep learning have been quickly developed recently. With regard to the heuristic methods, the road feature model is first introduced, then, the classic extraction methods are reviewed in two subcategories: semiautomatic and automatic. The principles, inspirations, advantages, and disadvantages of these methods are described. In terms of the data-driven methods, the road extraction methods based on deep neural network, particularly those based on patched convolutional neural network, fully convolutional network, and generative adversarial network are reviewed. We perform subjective comparisons between the methods inner each type. Furthermore, the quantity performances achieved on the same dataset are compared between the heuristic and data-driven methods to show the strengthening of the data-driven methods. Finally, the conclusion and prospects are summarized.

DeepWindow: Sliding Window Based on Deep Learning for Road Extraction From Remote Sensing Images

The road centerline extraction is the key step of the road network extraction and modeling. The hand-craft feature engineering in the traditional road extraction methods is unstable, which makes the extracted road centerline deviated from the road center in complex cases and even results in overall extracting errors. Recently, the road centerline extraction methods based on semantic segmentation employing deep neural network greatly outperformed the traditional methods. Nevertheless, the pixel-wise labels for training deep learning models are expensive and the postprocess of road segmentation is error-prone. Inspired by the work of human pose estimation, we propose DeepWindow, a novel method to automatically extract the road network from remote sensing images. DeepWindow uses a sliding window guided by a CNN-based decision function to track the road network directly from the images without the prior of road segmentation. First of all, we design and train a CNN model to estimate the road center points inside a patch. Then, the road seeds are automatically searched patch by patch employing the CNN model. Finally, starting from seeds, our method first estimates the road direction using a Fourier spectrum analysis algorithm and then iteratively tracks the road center-line along the road direction guided by the CNN model. In our method, the CNN model is trained by point annotations, which greatly reduces the training costs comparing to those in semantic model training. Our method achieves comparable performance with the state-of-the-art road extraction methods, and extensive experiments indicate that our method is robust to the point deviation.