Design and Development of a Low-Cost Multispectral Imager for Data Fusion with Hyperspectral Imagers

The fusion of multispectral (MS) and hyperspectral (HS) data has been limited by the availability of space systems that have the capacity to host both imagers simultaneously. High-performing single instrumentation across a large wavelength range is both difficult from an engineering perspective as well as expensive. However, in recent years, external payload platforms on the International Space Station (ISS), such as Bartolomeo and Multi-User System for Earth Sensing (MUSES), have surfaced and created more possibilities for MS/HS data fusion to produce high resolution in both spectral and spatial domains. We present a proof-of-concept and breadboard for a low-cost MS imager that optimally complements a HS imager. Design characteristics of the MS imager prototype include the ability to produce high-spatial resolution images (similar to 2.5m Ground Sample Distance), robust communications allowing seamless integration into the ISS downlink system, data fusion algorithms and methods, on-board computational processing, and ground mission control. A cost-benefit-risk analysis of the MS imager design will provide insight into ideal MS/HS joint operation. As demonstrated by K. Perlmutter et al., a low-cost MS imager adds value to a hyperspectral data stream, thereby increasing the relevance of Earth Observation applications for public policy-makers and humanitarian organizations, a central goal of the John Glenn Humanitarian Observatory program in The Ohio State University Battelle Center for Science, Engineering, and Public Policy.