Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method.