Читать книгу Change Detection and Image Time-Series Analysis 1 - Группа авторов - Страница 24

As mentioned previously, the number of superpixels N and the compactness factor m need to be determined in the SLIC algorithm. Note that in practical implementations, the parameter m impacts less than N on the segmentation results. Therefore, after multiple trials, we fixed m = 30 in this work. The only focus is on the determination of the optimal segmentation scale parameter N . To this end, an unsupervised strategy is applied based on the analysis of the global entropy. Note that after the mean operation on each superpixel, the texture information in the segments will be relatively suppressed, which may have an influence on the following CD performance. The main idea of the used criterion is to evaluate the information maintained in the superpixel-level segmented image inherited from the original pixel-level image. Thus, the one-dimensional image entropy (Global Entropy, GE) (Han et al. 2008) is calculated based on multi-scale segmentation results, where the optimal segmentation scale is determined by analyzing the change of GE values:

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where n denotes the gray level and p = (pk)k=1,2,...,n contains the histogram counts of the first three bands of Y^'. It is worth noting that with the increasing N, GE values are expected to increase continuously and approach the value of the original image. The logarithmic function is then used to fit the GE results to estimate the threshold for the optimal segmentation scale. A detailed description of this step is provided in Table 1.1.

Table 1.1. Determination of the optimal segmentation scale based on GE analysis

Step 1: Initialize N, which is approximated as the smaller one in the rows and columns ofthe input image, and the segmentation scale interval is set approximately equal to (row +column)/20.

Step 2: Calculate the GE value of each segmented image under different searching scales.

Step 3: Fit the logarithmic function on the obtained GE results and calculate the gradient.

Step 4: Estimate the optimal segmentation scale by analyzing the gradient of GE values, where the convergence threshold TGE is defined approximately equal to 100/(row + column) based on the input image.