Читать книгу Change Detection and Image Time-Series Analysis 1 - Группа авторов - Страница 27

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.

Figure 1.8. 2D compressed change representation in the polar domain (Xuzhou dataset). For a color version of this figure, see www.iste.co.uk/atto/change1.zip

Figure 1.9. Determination of the optimal segmentation scale in the Xuzhou dataset. (a) GE values of different segmented scales; (b) logarithmic fitting results based on (a). For a color version of this figure, see www.iste.co.uk/atto/change1.zip