Land Use and Land Cover Classification Using Transfer Learning and Temporal Convolutional Networks on Low-Resolution Remote Sensing Images
Recently, low-resolution remote sensing (RS) images have received significant attention because of their widespread spatial coverage, minimum acquisition cost, quick transmission ability, and large-scale earth observation suitability. However, land-use and land-cover (LULC) classification using low-resolution satellite imagery remains challenging due to restricted spatial information, spectral similarity amongst land-cover classes, noise differences, and complex scene heterogeneity. Though recent deep learning-based models have exhibited effective outcomes, they still suffer from insufficient feature representation, inadequate contextual dependency learning, and minimal classification accuracy when processing low-resolution RS images. To resolve these issues, this study develops a lightweight feature extraction model with Temporal Convolutional Networks for low-resolution remote sensing image classification. The proposed model initially preprocesses the images to improve feature consistency and quality. The feature extraction phase then employs MobileNet-V2 to identify and represent relevant spatial patterns in RS images, followed by a temporal convolutional network for RSI classification, enabling effective modeling of sequential and contextual dependencies in spatial features. Furthermore, adaptive fine-tuning of model parameters is performed using an artificial rabbit optimization algorithm to enhance classification accuracy and convergence behavior. Extensive experimental evaluation of the LFEARO-LULCRSI model on the benchmark EuroSat Dataset from Sentinel-2 imagery demonstrates improved performance over existing methods, achieving an accuracy of 98.57%. An ablation study is also performed to examine the contribution of individual model components. The proposed model thus proves useful for effective geospatial analysis in agriculture, urban planning, disaster assessment, and sustainable environmental management, enhancing feature discrimination and contextual dependency learning in low-resolution satellite imagery.