Development of a Filter Selection System for a Four-Band SWIR Optical Payload for an Earth Remote Sensing Nanosatellite

Short-Wave Infrared (SWIR) remote sensing plays a significant role in environmental monitoring, agricultural analysis, and nanosatellite-based Earth observation applications. Existing remote sensing frameworks suffer from limitations such as inefficient spectral band selection, high computational complexity, and lack of intelligent optimization techniques for compact nanosatellite payload systems. This study aims to develop an intelligent filter selection system for a four-band SWIR optical payload using multispectral satellite imagery and deep learning (DL)-based optimization techniques. The proposed framework focuses on improving spectral feature extraction, environmental condition classification, and nanosatellite payload efficiency. A multispectral field image dataset was utilized for experimental analysis, where preprocessing techniques, including atmospheric correction and Z-score normalization, were applied. Mutual Information (MI)-based optimal band selection and SWIR filter mapping were performed to identify the significant spectral bands B05, B08, B11, and B12. Feature extraction was conducted using raw SWIR band values and spectral band ratios. A Vision Transformer (ViT) model was employed for environmental condition classification while the Whale Optimization Algorithm (WOA) was integrated to optimize model parameters and improve convergence performance. The proposed ViT–WOA framework achieved superior classification performance with 96.93% accuracy, 97.19% precision, 96.93% recall, 96.92% F1-score. and a Kappa coefficient of 0.9540. The framework effectively classified cloudy, rainy, and sunny environmental conditions with reduced classification loss and improved spectral feature learning efficiency. The proposed system demonstrated reliable SWIR spectral analysis and intelligent payload optimization for nanosatellite remote sensing applications. The integration of transformer-based learning and metaheuristic optimization provided an effective solution for efficient Earth observation and environmental monitoring systems.