Analysis of haze removal in various weather condition using decision tree and support vector machine algorithm.

The study's goal is to use Decision Tree (DT) and Making the Most of Structural Vector Machines on Accuracy haze removal under diverse weather circumstances (SVM). Materials and procedures: This experiment's data set consists of 30 photos for various techniques of haze reduction in remote sensing photographs. The sample size determined by Gpower is 20 per group. The classifier's accuracy and precision are tested and reported for both Decision Tree and SVM. Results: When the independent sample the T-test's two-tailed p-value of 0.023 (p0.005) demonstrated statistical significance. When compared to the Support Vector Machine method, the Decision Tree method fared better. technique in terms of accuracy (80% vs. 80%). 85.5%. haze removal analysis under different weather situations. [ABSTRACT FROM AUTHOR]