Change Detection and Image Time-Series Analysis 1. Группа авторов

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Change Detection and Image Time-Series Analysis 1 - Группа авторов


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The second and third inputs are generated at the superpixel level. The change magnitude of superpixels is calculated on Y', and binary CD results are obtained using Bayesian-EM thresholding and fuzzy c-means (FCM) clustering, respectively. A three-input majority voting decision is applied by integrating three binary CD results from a pixel-to-superpixel perspective. Note that the final binary CD decision is made on each superpixel, depending on the majority label of three independent inputs, as shown in Figure 1.5. FCM clustering is then applied on the variable θ but only focuses on the changed superpixels, with the given estimated number of clusters equal to K in the 2D compressed polar domain D, as shown in Figure 1.2.

      1.4.1. Dataset description

      1.4.2. Experimental setup

      For comparison purposes, CD results obtained by the proposed M2C2VA and SPC2VA approaches were compared with two state-of-the-art unsupervised multiclass CD techniques, including the iteratively reweighted multivariate alteration detection (IR-MAD) (Nielsen 2007), and the sequential spectral change vector analysis (S2CVA) (Liu et al. 2015). Note that M2C2VA and SPC2VA considered both spectral and spatial change information, whereas IR-MAD and S2CVA considered only the spectral change information. Detailed quantitative and qualitative analyses were conducted according to the obtained CD accuracy, i.e. OA and Kappa, and error indices, i.e. omission errors (OE), commission errors (CE), total errors (TE), and the obtained CD maps. In addition, the computational cost was also considered in each method and compared. All of the experiments were conducted using MATLAB R2016b, on an Intel (R) Core (TM) i7-6700 CPU @ 3.40GHz PC with 32 GB of RAM.

Schematic illustration of 2D compressed change representation in the polar domain.

      1.5.1. Results on the Xuzhou dataset


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