Table 6 Detailed procedure of information fusion process

1: Initialize 2d Matrix Categories.Labels[][]

2: IMGLabels = Image.labels

3:   FOR all Labels in Categories.Labels[Rows][Columns] DO

4:     FOR all Labels in IMGLabels DO

5:       IF current_label in IMGlabels € Categories.Labels[current_Row]

6:          Matrix_frequency[current_Row].append(SUM(current_label in IMGLabels))

7: X = count_of_unique_labels in IMGLabels

8:   FOR Rows in Matrix_frequency[][]DO

9:     Categories(wt(global))[Current_Row] = \({{\varvec{C}}}_{{\varvec{i}}=1:6}\left({\varvec{w}}{\varvec{t}}\left({\varvec{g}}{\varvec{l}}{\varvec{o}}{\varvec{b}}{\varvec{a}}{\varvec{l}}\right)\right)\)

10:   FOR Rows in Matrix_frequency[][] DO

11:     FOR Columns in Matrix_frequency[][] DO

12:      Categories(wt(local)) [Current_Row] = (\({{\varvec{C}}}_{{\varvec{i}}=1:6}({\varvec{w}}{\varvec{t}}({\varvec{l}}{\varvec{o}}{\varvec{c}}{\varvec{a}}{\varvec{l}})))\)

13:   FOR i = 0 to (Number of Rows in Matrix_frequency-1) DO

14:     Product[i] = (Categories(wt(global))[i]* Categories (wt(local))[i])

15: Disaster_category = max(Product[])

16: IF Index(Categories.Labels) =  = Index(Disaster_category)

17:     Output(Category_Name)