Enhanced Land Use and Land Cover Classification Through Human Group-based Particle Swarm Optimization-Ant Colony Optimization Integration with Convolutional Neural Network

Abstract

Reliable classification of Land Use and Land Cover (LULC) using satellite images is essential for disaster management, environmental monitoring, and urban planning. This paper introduces a unique method that combines a Convolutional Neural Network (CNN) with Human Group-based Particle Swarm Optimization (HPSO) and Ant Colony Optimization (ACO) algorithms to improve the accuracy of LULC classification. The suggested hybrid HPSO-ACO-CNN architecture effectively solves the issues with feature selection, parameter optimization, and model training that are present in conventional LULC classification techniques. During the initial phases, HPSO and ACO are crucial in identifying the best hyperparameters for the CNN model and fine-tuning the selection of critical spectral bands. ACO modifies the CNN's hyperparameters (learning rate, batch size, and convolutional layers), whereas HPSO finds the optimal selection of spectral bands. This optimization technique reduces the probability of overfitting while substantially enhancing the model's ability to generalize. Utilizing the selected spectral bands and optimum parameter configuration, the CNN algorithm is trained in the second phase. With Python implementation, this method uses both the spatial and spectral characteristics that the CNN detects to reach an outstanding 99.3% accuracy in LULC classification. The hybrid approach outperforms traditional methods like Deep Neural Network (DNN), Multiclass Support Vector Machine (MSVM), and Long Short-Term Memory (LSTM) in experiments using benchmark satellite image datasets, demonstrating a significant 10.5% increase in accuracy. This hybrid HPSO-ACO-CNN architecture transforms accurate and dependable LULC classification, offering an advantageous instrument for remote sensing applications. It enhances the area of satellite imagery evaluation by combining the advantages of deep learning techniques with optimization algorithms, enabling more accurate mapping of land use and cover for sustainable land management and environmental preservation.