To address this we consider a 2D mosaic filters sampling scheme to acquire an incomplete multispectral data cube on a single frame readout from a focal plane array. Specifically, the sparse data cube contains 4 x 4 spatial cells and 16 wavebands with each waveband sampled once per cell; this corresponds to a 1/16 undersampling of the data cube. Complete multispectral images are then computed using compressed sensing protocols.
Results obtained using hyperspectral datasets from AVIRIS and Stanford University (SCIEN) are presented to demonstrate image reconstruction using 16 wavebands in the visible and near infrared. The function of the mosaic filter is mimicked by sampling the full dataset according to the design of a theoretical mosaic filter. This allows us to investigate different sampling strategies and, in particular, make a direct comparison between random and regular sampling. Our results show that the reconstruction error is strongly dependent on both the colour content and the sampling strategy in the test images, and that very good reconstruction can be achieved approaching the spatial resolution of the original image. Our results can be applied to both the MWIR and LWIR where the lower spectral resolution means that a smaller number of wavebands is likely to be sufficient for identification and tracking. The concept can also be extended to polarimetric imaging with a suitable polarimetric filter mask to provide a dual-mode polarimetric-multispectral imaging capability. This paper presents an overview of the technical approach and the general conclusions.