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A maximum a posteriori (MAP) estimation method for improving the spatial resolution of a hyperspectral image using a higher resolution auxiliary image is extended to address several practical remote sensing situations. These include cases where: 1) the spectral response of the auxiliary image is unknown and does not match that of the hyperspectral image; 2) the auxiliary image is multispectral; and 3) the spatial point spread function for the hyperspectral sensor is arbitrary and extends beyond the span of the detector elements. The research presented follows a previously reported MAP approach that makes use of a stochastic mixing model (SMM) of the underlying spectral scene content to achieve resolution enhancement beyond the intensity component of the hyperspectral image. The mathematical formulation of a generalized form of the MAP/SMM estimate is described, and the enhancement algorithm is demonstrated using various image datasets.