skip to main content
Idioma:

Comparison of GEOBIA classification algorithms based on Worldview-3 imagery in the extraction of coastal coniferous forest

Panđa, Lovre ; Milošević, Rina ; Šiljeg, Silvija ; Domazetović, Fran ; Marić, Ivan ; Šiljeg, Ante

Šumarski list (1945), 2021-12, Vol.145 (11-12), p.535-544 [Periódico revisado por pares]

Texto completo disponível

Citações Citado por
  • Título:
    Comparison of GEOBIA classification algorithms based on Worldview-3 imagery in the extraction of coastal coniferous forest
  • Autor: Panđa, Lovre ; Milošević, Rina ; Šiljeg, Silvija ; Domazetović, Fran ; Marić, Ivan ; Šiljeg, Ante
  • É parte de: Šumarski list (1945), 2021-12, Vol.145 (11-12), p.535-544
  • Descrição: Šume primorskih četinjača, sa svojom ekološkom, ekonomskom, estetskom i društvenom funkcijom, predstavljaju važan dio europskih šumskih zajednica. Osnovni cilj ovoga rada je usporediti najkorištenije GEOBIA (engl. Geographic Object-Based Image Analysis ) klasifikacijske algoritme (engl. Random Trees – RT, Maximum Likelihood – ML, Support Vector Machine – SVM ) s ciljem izdvajanja šuma primorskih četinjača na visoko-rezolucijskom WorldView-3 snimku unutar topografskog slijevnog područja naselja Split. Metodološki okvir istraživanja uključuje (1) izvođenje izoštrenog multispektralnog snimka ( WV-3MS -a) ; (2) testiranje segmentacijskih korisničko-definiranih parametara; (3) dodavanje testnih uzoraka; (4) klasifikaciju segmentiranog modela; (5) procjenu točnosti klasifikacijskih algoritama, te (6) procjenu točnosti završnog modela. RT se prema korištenim pokazateljima ( correctness – COR , completeness – COM i overall quality – OQ ) pokazao kao najbolji algoritam. Iterativno postavljanje segmentacijskih parametara omogućilo je detekciju najprikladnijih vrijednosti za generiranje segmentacijskog modela. Utvrđeno je da sjene mogu uzrokovati značajne probleme ako se klasificiranje vrši na visoko-rezolucijskim snimkama. Modificiranim Cohen’s kappa coefficient (K) pokazateljem izračunata je točnost konačnog modela od 87,38%. WV-3MS se može smatrati kvalitetnim podatkom za detekciju šuma primorskih četinjača primjenom GEOBIA metode. With their ecological, economic, aesthetic, and social function, coniferous forests represent an important part of European forest communities. The main objective of this paper is to compare the most used GEOBIA (Geographic Object-Based Image Analysis) classification algorithms (Random Trees - RT, Maximum Likelihood - ML, Support Vector Machine - SVM) for the purposes of the coastal coniferous forest detection on a high-resolution WorldView-3 (WV-3) imagery on the topographic basin of the Split settlement (Figure 1). The methodological framework (Figure 2) includes: (1) 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 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.
  • Idioma: Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.