Enhancing Land Cover/Land Use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN)

Abstract

Sustainable natural resources management relies on effective and timely assessment of conservation and land management practices. Using satellite imagery for Earth observation has become essential for monitoring land cover/land use (LCLU) changes and identifying critical areas for conserving biodiversity. Remote Sensing (RS) datasets are often quite large and require tremendous computing power to process. The emergence of cloud-based computing techniques presents a powerful avenue to overcome computing limitations by allowing machine-learning algorithms to process and analyze large RS datasets on the cloud. Our study aimed to classify LCLU for the Talassemtane National Park (TNP) using a Deep Neural Network (DNN) model incorporating five spectral indices to differentiate six land use classes using Sentinel-2 satellite imagery. Optimization of the DNN model was conducted using a comparative analysis of three optimization algorithms: Random Search, Hyperband, and Bayesian optimization. Results indicated that the spectral indices improved classification between classes with similar reflectance. The Hyperband method had the best performance, improving the classification accuracy by 12.5% and achieving an overall accuracy of 94.5% with a kappa coefficient of 93.4%. The dropout regularization method prevented overfitting and mitigated over-activation of hidden nodes. Our initial results show that machine learning (ML) applications can be effective tools for improving natural resources management.