Road extraction from SAR imagery based on an improved particle filtering and snake model

Liu, Junyi; Sui, Haigang; Tao, Mingming; Sun, Kaimin; Mei, Xin

doi:10.1080/01431161.2013.835082

Cited by 14 publications

(4 citation statements)

References 21 publications

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“…If the individual road features in images can be featured as having (a) a relatively constant width, (b) a uniform surface intensity, or (c) a path that is locally well fitted using a parabola (McKeown, David, and Denlinger 1988;Da Silva and Centeno 2012). Then, ad-hoc methods can be applied to focus on using these features and attempt to follow predefined modelling methodologies (Liu et al 2013;Wang and Zhang 2011).…”

Section: Introductionmentioning

confidence: 99%

Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine

Wang

Song

Chen

et al. 2015

International Journal of Remote Sensing

145

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Extracting road networks from very-high-resolution (VHR) aerial and satellite imagery has been a long-standing problem. In this article, a neural-dynamic tracking framework is proposed to extract road networks based on deep convolutional neural networks (DNN) and a finite state machine (FSM). Inspired by autonomous mobile systems, the authors train a DNN to recognize the pattern of input data, which is an image patch extracted in a detection window centred at the current location of the tracker. The pattern is predefined according to the environment and associated with the states in the FSM. A vector-guided sampling method is proposed to generate the training data set for the DNN, which extracts massive image-direction pairs from the imagery and existing vector road maps. In the tracking procedure, the size of the detection window is determined by a fusion strategy and the extracted image patches represent the orientation features of the road (local environment) that can be recognized by the trained DNN. The reactive unit in FSM associates states with behaviours of the tracker while continually modifying the orientation to follow the road and generating a sequence of states and locations. In this way, our framework combines the DNN and FSM. DNN acts as a key component to recognize patterns from a complex and changing environment; FSM translates the recognized patterns to states and controls the behaviour of the tracker. The results illustrate that our approach is more accurate and efficient than the traditional ones.

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Section: Introductionmentioning

confidence: 99%

Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine

Wang

Song

Chen

et al. 2015

International Journal of Remote Sensing

145

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“…They updated the road vectors by using the airborne SAR image as well as the existing road database which was used as the initial value for each snake. Liu et al (2013) presented a road extraction method from the SAR imagery based on an improved particle filtering and a snake model.…”

Section: Introductionmentioning

confidence: 99%

A Method for Automatic Road Extraction of High Resolution SAR Imagery

Saati

Amini

Maboudi

2015

J Indian Soc Remote Sens

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Nowadays automatic road extraction from satellite imageries is considered as one of the most important research trends in the field of remote sensing. This paper presents a method for automatic extraction of road centerlines from synthetic aperture radar (SAR) imagery. During the first step, three features, namely the direction of the least total radiance, the corresponding radiance, and the contrast are extracted to define the road characteristics by the backscatter coefficient of each pixel and its neighboring pixels from the SAR imagery. The fusion of the extracted features is carried out in the next step for detection of the road areas by using a fuzzy inference system. Afterwards, the morphology skeletonization is applied on the road areas to extract the road skeleton. Then some interested seed points are extracted so that they could be used in a snake model, which is employed to connect the seed points in order to form up the road centerlines. The proposed algorithm is tested on different parts of TerraSAR-X images. The experimental results reveal that the proposed method is effective in terms of correctness, completeness, and quality.

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“…Since extracting road by manual processing is too expensive and time consuming, research based on automatic and semiautomatic has become more and more popular in recent years. Such interest is motivated by the requirements for civilian and military applications, such as road maps, traffic monitoring, navigation applications, and topographic mapping (Liu, J.Y, 2013). The main problem of fully automatic approach is that it needs strict hypothesis of road characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…As fully automatic methods for mapping are still far out of reach, semi-automatic methods for feature extraction that interact with a human operator are considered to be a good compromise between the calculation speed of a computer algorithm and the interpretation skills of the operator. In (Liu, J.Y, 2013), an improved particle filtering and snake model is utilized to extract roads in SAR images. In (Lin, X.G, 2012), using parallel angular texture signature get the moving direction of current road centreline point for the medium and low resolution imagery.…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic Road Extraction from SAR images using EKF and PF

Zhao

Yang

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recently, the use of linear features for processing remote sensing images has shown its importance in applications. As one of typical linear targets, road is a hot spot of remote sensing image interpretation. Since extracting road by manual processing is too expensive and time consuming, researches based on automatic and semi-automatic have become more and more popular. Such interest is motivated by the requirements for civilian and military applications, such as road maps, traffic monitoring, navigation applications, and topographic mapping. How to extract road accurately and efficiently from SAR images is a key problem. In this paper, through analyzing characteristics of road, semi-automatic road extraction based on Extend Kalman Filtering (EKF) and Particles Filtering (PF), is presented. These two methods have the same algorithm flow which is an iterative approach based on prediction and update. The specific procedure as follows: at prediction stage, we obtain prior probability density function by the prior stage and prediction model, and through prior probability density function and the new measurement, at update stage we obtain the posterior probability density function which is the optimal estimation of road system state. Both EKF and PF repeat the steps above until the extracting tasks are finished. We use these two methods to extract road respectively. The effectiveness of the proposed method is demonstrated through the experiments from Howland by UAVSAR in L-band. And through contrast experiments, we discover that extracting difference complexity of road based on different methods can improve accuracy and efficiency. The results show that EKF has better performance on road with middle noise and PF has better performance on road with high noise.

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Road extraction from SAR imagery based on an improved particle filtering and snake model

Cited by 14 publications

References 21 publications

Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine

Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine

A Method for Automatic Road Extraction of High Resolution SAR Imagery

Semi-automatic Road Extraction from SAR images using EKF and PF

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