Multi-Spectral Efficient Land Use Classification (MELC)

Land use classification is a crucial component of the field of remote sensing due to its importance in the use and development of agricultural, commercial, and industrial applications, while also aiding in environmental planning and conservation. This paper introduces two innovative iterations of Multispec- tral Efficient Land Use Classification (MELC) based on vision transformers (ViTs), namely, MELC and MELC with PreCoding (MELC-PC). The MELC model utilizes an extremely compact vision transformer design characterized by tightly constrained dense layers and low-rank convolutional filters of small kernel sizes. Both models demonstrate state-of-the-art results, showcasing not only exceptional accuracy but also remarkable computational efficiency compared to existing models and techniques. The results demonstrate that both the MELC models achieve unprecedented efficiency and accuracy results in multispectral image classification.