In practical operation, given the upper cut-set threshold tu and the lower cut-set threshold td, if fuzzy membership [[mu].
0)]; determine the upper cut-set tu and the lower cut-set td; set the fuzzy weighting exponent m, the end iteration error e, the initial number of iterations (t = 0), and the maximum number of iterations [t.
Compared with other methods, the proposed method has the following features: (1) upper and lower cut-sets are introduced to improve the categorizing rate when using spatial fuzzy c-means (sFCM) algorithm to solve color aliasing and false colors; (2) it deals with the problem of thick lines by removing node segments, with more accurate results; (3) according to the causes of gaps, different methods are used to repair gaps to obtain maps with continuous and complete contour lines.
Considering that the introduction of spatial neighborhood information adds to the time complexity of the algorithm to some extent, upper and lower cut-sets are introduced to dynamically adjust the convergence rate of elements with varying membership degrees, which raises the convergence rate of those with higher membership degrees and reduces the impact of low membership degrees on cluster centers.
Perform calculations according to formula (2) for the new membership matrix, and treat it with the upper and lower cut-sets.