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ŠUMARSKI LIST 11-12/2021 str. 39     <-- 39 -->        PDF

derivation of a sharpened multispectral image (WV-3MS) (Figure 3); (2) testing of the user-defined parameters in segmentation process (Figure 4); (3) marking of test samples (signatures); (4) classification of a segmented model; (5) accuracy assessment of the classification algorithms, and (6) accuracy assessment of the final model. The developed ACP tool (Automated Classification Process) (Supplement figure 5) for speeding up the entire classification process, enabled the simultaneous generation of output results for three selected classification algorithms (RT, ML and SVM) (Figure 6). Metric indicators (correctness - COR, completeness - COM, and overall quality - OQ) have shown that RT is the most accurate classification algorithm for the coastal coniferous forest detection (Table 1; Figure 7). The iterative setting of segmentation parameters enabled the detection of the most optimal values &8203;&8203;for generating a segmentation model. It is found that shadows can cause significant problems if classification is done on high-resolution images (Figure 8). The solution may be to collect a larger number of samples in different areas for the purpose of more detailed class differentiation. The modified Cohen’s kappa coefficient (K) indicator shown the accuracy of the final model of 87.38% (Table 2; Figure 9). WV-3MS can be considered as very good data for the detection of coniferous forests using the GEOBIA method (Figure 10). According to this research, 31.36% of the Split topographic basin is covered by highly and extremely flammable vegetation.
Key words: GEOBIA, WorldView-3, Coniferous Forest, Random Trees, Maximum Likelihood, Support Vector Machine.
Dodatna slika 5. Automated Classification Process (ACP) alat izrađen u Model Builder-u Supporting Information
Supplement figure 5. Automated Classification Process (ACP) tool created in Model Builder