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

Читать онлайн книгу.

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


Скачать книгу
this section, we propose an unsupervised superpixel-level compressed change vector analysis (SPC2VA) approach for multiclass CD. The traditional pixel-level spectral change analysis is converted into the superpixel level. Therefore, the spectral change representation and identification are regularized and enhanced under the superpixel constraints. The block scheme of the proposed approach is shown in Figure 1.4.

      1.3.3.1. Superpixel-level spectral change representation

      The general idea of the SLIC algorithm is to find small regional clusters by considering their local homogeneity (Achanta et al. 2012). The key step is to calculate the distance d that implements a measurement from the spectral–spatial point of view. Let dcolor and dxy be the spectral and spatial distances between two given pixels α and β, respectively, defined as:

      [1.14]image

      [1.15]image

      Here, (L, A, B)T denotes the CIELAB color space values, with L being the color lightness and A and B representing color values along red-green and blue-yellow axes, respectively. (x, y)T denotes the coordinates of a given pixel. A final weighted distance measure dαβ can be defined as:

      [1.16]image

      where s is the width of grids. It controls the size of created superpixels, i.e. the greater the s, the larger the superpixels. A roughly equal-sized grid interval can be defined as images where Z is the total number of pixels and N is the desired number of superpixels. In reality, the real number of generated superpixels (defined as N′) might be slightly different from N. The parameter m controls the relative importance between the color similarity and the spatial proximity. The greater the m, the greater the emphasis on spatial proximity and the compactness of a generated superpixel. A regular m value can be defined within the range of [1, 40]. For more details, readers can refer to the paper by Achanta et al. (2012).

      1.3.3.2. Determination of the optimal segmentation scale

      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:

      [1.17]image

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.

      1.3.3.3. Decision fusion-based CD


Скачать книгу