QiSTH: Quantum‐Inspired Secure Multispectral Transformer Networks For Remote Sensing Data Harmonization

Secure data Harmonization in a heterogeneous remote sensing environment remains a critical issue across distributed geographical platforms. Multispectral and environmental sensors face challenges with sensor noise, calibration drift, and site‐specific variability. This work presents a quantum‐inspired secure multispectral Transformer network for secure data harmonization across three distinct locations. Multi‐channel remote sensing measurements have been performed to acquire metadata in resource‐constrained sensing environments. A quantum‐inspired attention mechanism integrated with a deep encoder followed by a Transformer‐based harmonization module leveraged for long‐range cross‐feature dependencies. A secure feature abstraction layer has been introduced to control data leakage and unauthorized inference. Results demonstrate consistent noise suppression and cross‐sensor alignment, achieving lower error rates and generalizing the proposed approach to Internet of Things (IoT) remote sensing environments.