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One of the most popular methods in remote sensing for gathering and evaluating satellite data is the classification of
images. Several categories exist for image classification techniques, including supervised and unsupervised classification,
pixel-based, object-based, and rule-based approaches. Each type of technique has pros and cons of its own. Choosing the
method that produces the best results is one of the issues with image classification. The "best" model for classifying images
relies on the particular task and the dataset used. The ideal classification technique is a crucial component in increasing
classification accuracy. The strengths and drawbacks of various models vary, so selecting one that is appropriate for the
job is critical. The main objective of this research is to analyze and compare the results of each classifier used, including
ISODATA, K-mean, Maximum likelihood, Minimum distance, Support vector machine, and Neural network then integrate
these different types of classification using the winners-takes-all classification approach in order to try to improve the
results. The classified images were assessed, and both the overall accuracy and kappa coefficient were calculated and gave
79.50%, 73.89%, 77.05%, and 84.98%, 86.53%, 87.18%, and 88.69% for ISODATA, K-means, Minimum distance (MD),
Maximum likelihood (MXL), Support vector machine (SVM), Neural network (NNT), and winner takes all (WTA),
respectively. From the results, the Winner takes all (WTA) presented a superior in terms of the overall accuracy and kappa
coefficient. |
