Using a dynamic arithmetic optimization approach to improve ridgelet neural network performance in remote sensing scene classification.

This research proposes an innovative methodology for acurate remore sensing scene classification. Here, a new design of dynamic arithmetic optimization algorithm (DAOA) has been proposed to enhance the performance of a Ridgelet neural network (RNN) in this purpose. The RNN is commonly used in image processing and computer vision, but its effectiveness can be hindered by subpar hyperparameter selection. To tackle this problem, the utilization of DAOA has been proposed as a robust optimization technique to automatically search for optimal hyperparameters in the RNN model. The proposed method has been assessed on UC Merced Land Use publicly available dataset frequently employed in remote sensing scene classification. The experimental results show that the proposed approach significantly enhances the efficiency of the RNN when compared to other cutting-edge methods. The findings indicate that the combination of optimization algorithms like DAOA with deep learning models such as RNNs has the potential to yield more precise and efficient solutions for remote sensing scene classification tasks.