Comparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs

Abstract

Our goal is to compare the performance of Classification and Regression Tree, Naive Bayes and Random Forest algorithms, from supervised image classification, and approaches on Pixel-Based Image analysis (PBIA) and Geographic Object-Based Image Analysis (GEOBIA), to classify turbidity in reservoirs. Tod do so, we use Landsat 8 image and bands and spectral indices, as predictive parameters, as well as the classification algorithms based on PBIA and GEOBIA. The Brazilian Itaipu reservoir was adopted, as a case study. Our results show that the RF classifier obtained the highest accuracy in both classification approaches, followed by CART and NB. The KA and OA indices of the GEOBIA classifications were superior to the PBIA classifications in both algorithms. This study contributes with an approach to quickly and accurately delineating turbidity spectral limits in reservoirs.

Keywords:

• Image segmentation
• turbidity
• reservoir

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The present work was carried out with the support of Brazilian Coordination for the Improvement of Higher-Level Personnel (CAPES) and the National Council for Scientific and Technological Development (CNPq), represented by the Research Productivity scholarship. Research Productivity Fellowship.