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Hyperspectral imagery is used for a wide variety of applications, including target detection, tracking, agricultural monitoring, and natural resources exploration. The main reason for using hyperspectral imagery is that images reveal spectral information about the scene that is not available in a single band. Unfortunately, many factors, such as the limitations of focal plane array technology, the inherent tradeoff in spatial versus spectral resolution, and the desire to achieve area coverage, degrade the spatial quality of these images. Recently, many algorithms are introduced in the literature to improve the resolution of hyperspectral images using coregistered high spatial resolution imagery such as panchromatic imagery. In this letter, we propose a new algorithm to enhance the spatial resolution of low-resolution hyperspectral bands using strongly correlated and coregistered high spatial resolution panchromatic imagery. The proposed algorithm constructs the superresolution bands corresponding to the low-resolution bands to enhance the resolution using a proposed local enhancement technique. The local enhancement is based on the least squares regression and the local correlation to improve the estimated interpolation of the spatial resolution. The introduced algorithm is considered as an improvement for Price's algorithm which uses the global correlation for the spatial resolution enhancement. In addition, numerous studies are conducted to investigate the effect of spatial window size for achieving the local enhancement in the estimation process. The experimental results, which are obtained using hyperspectral data derived from an airborne imaging sensor, are presented to verify the improvement by the proposed algorithm.