Three-Dimensional Semiautomatic Road Extraction From a High-Resolution Aerial Image by Dynamic-Programming Optimization in the Object Space

Póz, Aluir Porfírio Dal; Gallis, Rodrigo A. B.; Silva, J F C da

doi:10.1109/lgrs.2010.2047838

Cited by 20 publications

(3 citation statements)

References 8 publications

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“…Since this early attempt various groups have proposed semi-automated road tracking approaches, in which single roads (mostly in rural areas) are traced after manual selection of starting nodes. Technical implementations of this idea include Kalman filtering (Vosselman and de Knecht, 1995), extended Kalman filtering and/or particle filtering (Movaghati et al, 2010), heuristic rule-based tracing (Baumgartner et al, 2002), and shortest path computation by dynamic programming (Gruen and Li, 1995;Gruen and Li, 1997;Dal Poz et al, 2010;Dal Poz et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Road networks as collections of minimum cost paths

Wegner

Montoya-Zegarra

Schindler

2015

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Related Workmentioning

confidence: 99%

Road networks as collections of minimum cost paths

Wegner

Montoya-Zegarra

Schindler

2015

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Because of the different degrees of interaction, heuristic road extraction methods can be divided into semi-automatic and automatic extraction methods. The mainstream methods of the semi-automatic methods are the active contour model [18], dynamic programming [19][20][21], and template matching [22][23][24][25]. The semi-automatic methods require human intervention, so these methods are less efficient.…”

Section: Introductionmentioning

confidence: 99%

Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark

Zhang

Wang

et al. 2022

Remote Sensing

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Mountain roads are of great significance to traffic navigation and military road planning. Extracting mountain roads based on high-resolution remote sensing images (HRSIs) is a hot spot in current road extraction research. However, massive terrain objects, blurred road edges, and sand coverage in complex environments make it challenging to extract mountain roads from HRSIs. Complex environments result in weak research results on targeted extraction models and a lack of corresponding datasets. To solve the above problems, first, we propose a new dataset: Road Datasets in Complex Mountain Environments (RDCME). RDCME comes from the QuickBird satellite, which is at an elevation between 1264 m and 1502 m with a resolution of 0.61 m; it contains 775 image samples, including red, green, and blue channels. Then, we propose the Light Roadformer model, which uses a transformer module and self-attention module to focus on extracting more accurate road edge information. A post-process module is further used to remove incorrectly predicted road segments. Compared with previous related models, the Light Roadformer proposed in this study has higher accuracy. Light Roadformer achieved the highest IoU of 89.5% for roads on the validation set and 88.8% for roads on the test set. The test on RDCME using Light Roadformer shows that the results of this study have broad application prospects in the extraction of mountain roads with similar backgrounds.

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“…The multi-objective optimization of the practical problems requires [2] simple architecture, easy implementation, low complexity, and low [3] and high optimization efficiency. However, the above features are often contradictory [4].…”

Section: Introductionmentioning

confidence: 99%

Mobile adaptive crowd optimization scheme based on distance field

Tang

Yin

2016

J Embedded Systems

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For complexity and efficiency of the multi-objective optimization, we proposed the mobile distance field-driven adaptive crowd optimization algorithm. In space, we modify the surface parameters based on the corresponding changes of the distance field to obtain the moving target's moving track and moving surface. When the curve of the moving track is changed, the x axis and the y axis of the moving track are adjusted adaptively. In this paper, the moving process is divided into three processes: the target dynamic crowd control, the crowd model algorithm, and the predictive control of linear time domain based on the moving target prediction and crowd control algorithm. Then, the multi-objective optimization algorithm of moving objects is proposed by using the crowd model to predict the status and the position of the target. The experimental results show the high accuracy, low complexity, and high efficiency of the proposed optimization algorithm.

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Three-Dimensional Semiautomatic Road Extraction From a High-Resolution Aerial Image by Dynamic-Programming Optimization in the Object Space

Cited by 20 publications

References 8 publications

Road networks as collections of minimum cost paths

Road networks as collections of minimum cost paths

Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark

Mobile adaptive crowd optimization scheme based on distance field

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