Road extraction from remote sensing image based on marked point process with a structure mark library

Zhao, Quanhua; Wu, You; Wang, Huabin; Li, Yu

doi:10.1080/01431161.2020.1736731

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Fig. 16(a2) and (b2) are results from the algorithms proposed by [18] and [19], respectively. It can be seen in Fig.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…Interestingly, the point process (PP) based road extraction algorithm [15]- [17] is designed by combining the spectral, geometrical and network-structure characteristics of road, which has made many breakthroughs for extracting road accurately and completely. In the previous PP-based road extraction algorithms [18], [19], PP is used to model the location distribution of road, road segments are modeled by marking PP with local geometry of road, then a connected network model is constructed by constraining the relationships between road segments, and simultaneously a spectral measurement model of pixels covered by road segments is constructed according to the spectral characteristic of road. The previous algorithms can avoid interference of geometric noises greatly and extract the complete and connected road network, but their simulations are time-consuming and extraction results are not accurate enough especially at the crossroads.…”

Section: Introductionmentioning

confidence: 99%

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Clustering Point Process Based Network Topology Structure Constrained Urban Road Extraction From Remote Sensing Images

Zhao

Shen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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To extract complicated road network from remote sensing images on urban scenes, this article presents a clustering point process (CPP) based network topology structure constrained road extraction algorithm. Firstly, the CPP is constructed to model the feature points, such as endpoints, bends, and crossroads in a road system. Based on that, an initial network topology structure is constructed by connecting the points with lines. Then, according to the network structure characteristic and the spectral characteristic of road, a network topology structure constraining model and a spectral measurement constraining model are constructed, respectively. By combining the models above, a road extraction model is built under the framework of Bayes' theorem. Finally, to simulate from the road extraction model and extract an optimal road network, a solution strategy, reversible jump Markov Chain Monte Carlo (RJMCMC) simulation algorithm with related transfer operations, is designed according to the CPP and network topology structure. Several high-resolution remote sensing images on urban scenes are tested. According to a buffer evaluation method, and compared with the comparing algorithms, accuracy and extraction rate of results from the proposed algorithm are increased by 10.86% and 8.75% on average, respectively. It is proved that the proposed algorithm can extract the complicated road network effectively. Index Terms-Clustering point process (CPP), feature points, network topology structure, reversible jump Markov chain Monte Carlo (RJMCMC), urban road extraction.

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“…Fig. 16(a2) and (b2) are results from the algorithms proposed by [18] and [19], respectively. It can be seen in Fig.…”

Section: B Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering Point Process Based Network Topology Structure Constrained Urban Road Extraction From Remote Sensing Images

Zhao

Shen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, the applicability of the algorithm is greatly affected by the images of surrounding objects. Zhao et al [18] proposed a method based on the marked point process with local structure constraints. This method uses a random point process to define the position, and uses line segments as the mark to define the geometric structure.…”

Section: Non-deep Learning Methodsmentioning

confidence: 99%

Road Extraction in Mountainous Regions from High-Resolution Images Based on DSDNet and Terrain Optimization

Shen

et al. 2020

Remote Sensing

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High-quality road network information plays a vital role in regional economic development, disaster emergency management and land planning. To date, studies have primarily focused on sampling flat urban roads, while fewer have paid attention to road extraction in mountainous regions. Compared with road extraction in flat regions, road extraction in mountainous regions suffers more interference, due to shadows caused by mountains and road-like terrain. Furthermore, there are more practical problems involved when researching an entire region rather than at the sample level. To address the difficulties outlined regarding mountain road extraction, this paper takes Jiuzhaigou county in China as an example and studies road extraction in practical applications. Based on deep learning methods, we used a multistage optimization method to improve the extraction effect. First, we used the contrast limited adaptive histogram equalization (CLAHE) algorithm to attenuate the influence of mountain shadows and improve the quality of the image. Then the road was extracted by the improved DSDNet network. Finally, the terrain constraint method is used to reduce the false detection problem caused by the terrain factor, and after that the final road extraction result is obtained. To evaluate the effect of road extraction comprehensively, we used multiple data sources (i.e., points, raster and OpenStreetMap data) in different evaluation schemes to verify the accuracy of the road extraction results. The accuracy of our method for the three schemes was 0.8631, 0.8558 and 0.8801, which is higher than other methods have obtained. The results show that our method can effectively solve the interference of shadow and terrain encountered in road extraction over mountainous regions, significantly improving the effect of road extraction.

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“…It is a flexible methodology that has been extended for object extraction from images to arbitrarily shaped objects [24]. More recently, [25] have developed the approach for microscope images, [26] have used MPP's to automatically detect the locations of road segments and [27] have used it for visual perceptions. A survey of marked point processes applied to image analysis can be found in [28].…”

Section: Marked Point Processesmentioning

confidence: 99%

Design as a Marked Point Process

Quigley

Vasantha

Corney³

et al. 2021

Journal of Mechanical Design

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Although AI systems which support composition using predictive text are well established there are no analogous technologies for mechanical design. Motivated by the vision of a predictive system that interactively suggests features to designer, this paper describes the theory, implementation and assessment of an intelligent system that learns from a family of previous designs and generates inferences using a form of spatial statistics. The formalism presented, models 3D design activity as a ‘Marked Point Process’ that enables the probability of specific features being added at a particular locations to be calculated. Because the resulting probabilities are updated every time a new feature is added the predictions will become more accurate as a design develops. This approach allows the cursor position on a CAD model to implicitly define a spatial focus for every query made to the statistical model. The authors describe the mathematics underlying a statistical model that amalgamates the frequency of occurrence of the features in the existing designs of a product family. Having established the theoretical foundations of the work, a generic six step implementation process is described. This process is then illustrated for circular hole features using a statistical model generated from a dataset of hydraulic valves. The paper describes how the positions of each design's extracted hole features can be homogenized through rotation and scaling. Results suggest that within generic part families (i.e. designs with common structure) a marked point process can be effective at predicting incremental steps in the development of new designs.

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Road extraction from remote sensing image based on marked point process with a structure mark library

Cited by 9 publications

References 33 publications

Clustering Point Process Based Network Topology Structure Constrained Urban Road Extraction From Remote Sensing Images

Clustering Point Process Based Network Topology Structure Constrained Urban Road Extraction From Remote Sensing Images

Road Extraction in Mountainous Regions from High-Resolution Images Based on DSDNet and Terrain Optimization

Design as a Marked Point Process

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