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  • 1.
    Kusetogullari, Huseyin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Yavariabdi, Amir
    KTO Karatay University, TUR.
    Unsupervised Change Detection in Landsat Images with Atmospheric Artifacts: A Fuzzy Multiobjective Approach2018In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, Vol. 2018, p. 1-16, article id 7274141Article in journal (Refereed)
    Abstract [en]

    A new unsupervised approach based on a hybrid wavelet transform and Fuzzy Clustering Method (FCM) with Multiobjective Particle Swarm Optimization (MO-PSO) is proposed to obtain a binary change mask in Landsat images acquired with different atmospheric conditions. The proposed method uses the following steps: preprocessing,  classification of preprocessed image, and  binary masks fusion. Firstly, a photometric invariant technique is used to transform the Landsat images from RGB to HSV colour space. A hybrid wavelet transform based on Stationary (SWT) and Discrete Wavelet (DWT) Transforms is applied to the hue channel of two Landsat satellite images to create subbands. After that, mean shift clustering method is applied to the subband difference images, computed using the absolute-valued difference technique, to smooth the difference images. Then, the proposed method optimizes iteratively two different fuzzy based objective functions using MO-PSO to evaluate changed and unchanged regions of the smoothed difference images separately. Finally, a fusion approach based on connected component with union technique is proposed to fuse two binary masks to estimate the final solution. Experimental results show the robustness of the proposed method to existence of haze and thin clouds as well as Gaussian noise in Landsat images.

  • 2.
    Li, Bing
    et al.
    Kunming Univ, CHN.
    Cheng, Wei
    Kunming Univ, CHN.
    Bie, Yiming
    Harbin Inst Technol, CHN.
    Sun, Bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Capacity of Advance Right-Turn Motorized Vehicles at Signalized Intersections for Mixed Traffic Conditions2019In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, article id 3854604Article in journal (Refereed)
    Abstract [en]

    Right-turn motorized vehicles turn right using channelized islands, which are used to improve the capacity of intersections. For ease of description, these kinds of right-turn motorized vehicles are called advance right-turn motorized vehicles (ARTMVs) in this paper. The authors analyzed four aspects of traffic conflict involving ARTMVs with other forms of traffic flow. A capacity model of ARTMVs is presented here using shockwave theory and gap acceptance theory. The proposed capacity model was validated by comparison to the results of the observations based on data collected at a single intersection with channelized islands in Kunming, the Highway Capacity Manual (HCM) model and the VISSIM simulation model. To facilitate engineering applications, the relationship describing the capacity of the ARTMVs with reference to the distance between the conflict zone and the stop line and the relationship describing the capacity of the ARTMVs with reference to the effective red time of the nonmotorized vehicles moving in the same direction were analyzed. The authors compared these results to the capacity of no advance right-turn motorized vehicles (NARTMVs). The results show that the capacity of the ARTMVs is more sensitive to the changes in the arrival rate of nonmotorized vehicles when the arrival rate of the nonmotorized vehicles is 500(veh/h)similar to 2000(veh/h) than when the arrival rate is some other value. In addition, the capacity of NARTMVs is greater than the capacity of ARTMVs when the nonmotorized vehicles have a higher arrival rate.

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