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

Abstract

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.

DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

“…Some popular semi-automatic methods for road segmentation are the extended Kalman filter (EKF) [7], and the active contour model also known as snake [8]. By evaluating road features, Zhao et al [7] proposed an algorithm based on EKF. The EKF method performs better in road scenes of intermediate complexity.…”

“…By evaluating road features, Zhao et al. [7] proposed an algorithm based on EKF. The EKF method performs better in road scenes of intermediate complexity.…”

Enhancing Road Extraction in Large-Scale Complex Terrain through Multi-Source Remote Sensing Image Fusion and Optimization

“…SAR-based road extraction algorithms can be categorized into semi-automatic and fully automatic methods. Semi-automatic approaches involve human-computer interaction and include the snake model, particle filter, template matching, a mathematical morphology, and the extended Kalman filter [3][4][5][6][7]. In contrast, fully automatic methods require no human intervention and encompass techniques such as dynamic programming, Markov Random Field (MRF) models, Genetic Algorithms (GAs), and fuzzy connectedness [8][9][10][11].…”

Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images

“…Unlike the thin line shape that roads present in low-resolution images, roads in high-resolution images are continuous homogeneous regions, which means that roads can be extracted from these images more accurately [10,12]. However, due to the influence of 'different objects with similar spectra', different image resolutions, different road types, road occlusions, etc., the difficulty of designing road extraction algorithms is also increasing [13,14].…”