A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big in a parallel distributed way. This paper proposes novel CBIR system hyperspectral (HSI) repository on under guide unmixed spectral information, i.e., endmembers their associated fractional abundances, to retrieve scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement method operates accelerating flow. In addition, global standard equipped with large library software-as-a-service mode, providing users storage, management, services through web interfaces. Furthermore, implementation is incorporated into establish unmixing-based content system. The performance our proposed was verified terms both efficiency accuracy.