Optimizing convolutional neural networks using elitist firefly algorithm for remote sensing classification

This article explores the application of a new optimal convolutional neural network (CNN) to segment remote sensing. The paper designs a modified version of the firefly algorithm to provide an optimal structure of the CNN. The method is then applied to segment the remote-sensing images. The authors compare the proposed method with several other architectures on two multispectral remote sensing datasets, evaluating model performance and accuracy. The findings demonstrate that the projected CNN method provides the most efficient outcomes and can be a viable option for remote sensing analysis and classification due to their relative ease of training and high accuracy rates. Furthermore, the authors discuss the potential for further requests of CNNs in remote sensing statistics analysis.