Vision Transformer for Multispectral Satellite Imagery: Advancing Landcover Classification

Abstract

Climate change is a global issue with significant impacts on ecosystems and human populations. Accurately classifying land cover from multi-spectral satellite imagery plays a crucial role in understanding the Earth's changing landscape and its implications for environmental processes. However, traditional methods struggle with challenges like limited data availability and capturing complex spatial-spectral relationships. Vision Transformers have emerged as a promising alternative to convolutional neural networks (CNN architectures), harnessing the power of self-attention mechanisms to capture global and long-range dependencies. However, their application to multi-spectral images is still limited. In this paper, we propose a novel Vision Transformer designed for multi-spectral satellite image datasets of limited size to perform reliable land cover identification with forty-four classes. We conduct extensive experiments on a curated dataset, simulating scenarios with limited data availability, and compare our approach to alternative architectures. The results demonstrate the potential of our Vision Transformer-based method in achieving accurate land cover classification, contributing to improving climate change modeling and environmental understanding.