Remote Sensing-Based Land Use and Land Cover Classification Using Deep Learning with Tuna Swarm Optimisation for Hyperparameter Tuning Process

Land Use and Land Cover (LULC) are key indicators of global environmental change. As a result, the extensive effort was dedicated to creating larger-scale products of LULC from Remote Sensing (RS) data, allowing the technical group to utilize these products for a wide array of downstream applications. This phenomenon causes widespread anxiety about natural resources. Therefore, observing LULC changes was significant for natural resource management and evaluating the effects of environmental change. Machine Learning (ML) has recently gained significance for fast and accurate LULC mapping using RS data, driven by the growing requirement for ecological, environmental, and resource management. It is crucial to compute the performance of diverse ML models for reliable LULC mapping. This study proposes a novel Remote Sensing-Based Land Use and Land Cover Classification Using Deep Learning with Tuna Swarm Optimisation (RSLULCC-DLTSO) methodology. The RSLULCC-DLTSO methodology aims to advance intelligent and automated LULC classification systems that assist in sustainable land management and environmental decision-making. In the pre-processing stage, the RSLULCC-DLTSO technique utilizes a Wiener Filtering (WF) model to eliminate noise and enhance the quality of satellite images. Furthermore, the DenseNet-121-based feature extraction captures hierarchical spatial patterns and textures from RSI. A Variational Autoencoder (VAE) model is also used for LULC classification. Finally, the Tuna Swarm Optimisation (TSO) model optimally adjusts the hyperparameter values of the VAE technique, resulting in improved classification performance. A wide range of simulation analyses of the RSLULCC-DLTSO approach is implemented under the EuroSat dataset. The comparative study of the RSLULCC-DLTSO approach illustrated a superior accuracy value of 98.57% compared to existing models.