A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery.

The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory. Through testing on the Aerial Image Dataset the and UC Merced Land Use Dataset and comparing it with other published works, the approach showed higher classification accuracy compared to current state-of-the-art methods. The study revealed that incorporating boosted Big-Bang Crunch significantly enhances the performance of stacked autoencoder in land-use classification tasks. Moreover, comparisons with other techniques, including convolutional neural networks, Cascaded Residual Dilated Networks, hierarchical convolutional recurrent neural networks, Fusion Region Proposal Networks, and multi-level context-guided classification techniques using Object-Based Convolutional Neural Networks, emphasized the benefits of using Convolutional Neural Network models over traditional methods. The proposed model achieved an accuracy of 92.49% on the AID dataset and 95.93% on the UC Merced dataset, with precision scores of 98.64% and 98.93%, respectively. These results emphasize the importance of integrating deep learning architectures with sophisticated optimization techniques, contributing to enhanced land-use classification accuracy.