Hyperspectral image quality for unmixing and subpixel detection applications

The quality of remotely sensed hyperspectral images is not easily assessed visually, as the value of the imagery is primarily inherent in the spectral information embedded in the data. In the context of earth observation or defense applications, hyperspectral images are generally defined as high spatial resolution (1 to 30 meter pixels) imagery collected in dozens to hundreds of contiguous narrow (~100) spectral bands from airborne or satellite platforms. Two applications of interest are unmixing which can be defined as the retrieval of pixel constituent materials (usually called endmembers) and the area fraction represented by each, and subpixel detection, which is the ability to detect spatially unresolved objects. Our approach is a combination of empirical analyses of airborne hyperspectral imagery together with system modeling driven by real input data. Initial results of our study show the dominance of spatial resolution in determining the ability to detect subpixel objects and the necessity of sufficient spectral range for unmixing accuracy. While these results are not unexpected, the research helps to quantify these trends for the situations studied. Future work is aimed at generalizing these results and to provide new prediction tools to assist with hyperspectral imaging sensor design and operation.