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Road information change detection based on fractional integral and neighborhood FCM
KTH.
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2018 (English)In: Chang'an Daxue Xuebao (Ziran Kexue Ban)/Journal of Chang'an University (Natural Science Edition), ISSN 1671-8879, Vol. 38, no 2, p. 103-111Article in journal (Refereed) Published
Abstract [en]

In order to improve the accuracy of road information change detection, a new road information change detection method based on fractional integral and spatial neighborhood fuzzy C-means (FCM) algorithm was presented. Firstly, a new difference image was generated by the gray difference calculation of the dual phase remote sensing images after registration and geometric correction. Then, a smaller fractional integral order was used to construct the denoising image mask with eight directions on the upper and lower, left and right, and four diagonals, and the fractional integral calculation were applied to the difference images, which improved the image signal-to-noise ratio (SNR) while preserving the edge and texture details of the image. Finally, the FCM clustering method combined with neighborhood spatial information was used to calculate the difference image after denoising. The highest and lowest points of the difference image gray values were selected as the center point of cluster initialization. The Euclidean Metric of the neighborhood were used to depict different weight values, so as to characterize the influence degree of domain pixels on central pixels and eliminate invalid isolated points. Detecting probability, false alarm rate and missed alarm rate of the algorithm were evaluated by the experiment. The results show that FCM road information change detection method based on fractional integral and neighborhood spatial information can effectively extract road change information. When the integral fractional order is 0.2, the FCM smoothing parameter is 2.5, the detection probability is higher than the comparison algorithm by 18% to 46%, the false alarm rate is lower than the comparison algorithm by 15% to 38%, and the missed alarm rate is lower than the comparison algorithm by 3% to 7%. The present algorithm can achieve better results in suppressing noise information and enhancing texture details. Especially, when the center pixel is noise, due to the introduction of neighborhood information, and it is affected by the neighborhood normal pixels. The proposed method could avoid misclassification by adjusting the membership automatically, it can effectively suppress the influence of neighborhood noise points on the normal pixel classification, and reduce the false alarm rate. 2 tabs, 4 figs, 28 refs. 

Place, publisher, year, edition, pages
Editorial Department of Journal of Chang'an University (Natural Science Edition) , 2018. Vol. 38, no 2, p. 103-111
Keywords [en]
Change detection, Fractional integral, Fuzzy C-means clustering, Road information, Traffic engineering
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-252265Scopus ID: 2-s2.0-85062659148OAI: oai:DiVA.org:kth-252265DiVA, id: diva2:1345309
Note

QC20190823

Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-08-23Bibliographically approved

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