Generation of remotely sensed reference data using low altitude, high spatial resolution hyperspectral imagery

Exploitation of imaging spectroscopy (hyperspectral) data using classification and spectral unmixing algorithms is a major research area in remote sensing, with reference data required to assess algorithm performance. However, we are limited by our inability to generate rapid, accurate, and consistent reference data, thus making quantitative algorithm analysis difficult. As a result, many investigators present either limited quantitative results, use synthetic imagery, or provide qualitative results using real imagery. Existing reference data typically classify large swaths of imagery pixel-by-pixel, per cover type. While this type of mapping provides a first order understanding of scene composition, it is not detailed enough to include complexities such as mixed pixels, intra-endmember variability, and scene anomalies. The creation of more detailed ground reference data based on field work, on the other hand, is complicated by the spatial scale of common hyperspectral data sets. This research presents a solution to this challenge via classification of low altitude, high spatial resolution (1m GSD) National Ecological Observatory Network (NEON) hyperspectral imagery, on a pixel-by-pixel basis, to produce sub-pixel reference data for high altitude, lower spatial resolution (15m GSD) AVIRIS imagery. This classification is performed using traditional classification techniques, augmented by (0.3m GSD) NEON RGB data. This paper provides a methodology for generating large scale, sub-pixel reference data for AVIRIS imagery using NEON imagery. It also addresses challenges related to the fusion of multiple remote sensing modalities (e.g., different sensors, sensor look angles, spatial registration, varying scene illumination, etc.). A new algorithm for spatial registration of hyperspectral imagery with disparate resolutions is presented. Several versions of reference data results are compared to each other and to direct spectral unmixing of AVIRIS data. Initial results are promising, with ground based surveying required to quantify the accuracy of “remotely sensed reference data.”