Comparison of SAR image classification using novel convolutional neural network with random forest for enhancing accuracy

The primary goal of this study is to compare a new convolutional neural network with a random forest model in order to improve the accuracy of SAR picture categorization. Approach and methodology: Twenty samples from the dataset were extracted using the Kaggle platform. Twenty datasets were used; ten were utilized for training purposes, and ten were reserved for testing. We use MATLAB to categorize synthetic aperture radar images and evaluate the detection accuracy of random forests and new convolutional neural networks. After zeroing in on an alpha value of 0.05 and an 80% power, the G power is used to calculate the sample size. Using MATLAB as our model, we found that the unique convolutional neural network technique achieved a classification accuracy of 95.31%, which was higher than the random forest model. The random forest model achieved a mere 71.06%. A level of significance of 0.038 (p