Assessment of Hyperspectral Sharpening Methods for the Monitoring of Natural Areas Using Multiplatform Remote Sensing Imagery

The use of cutting-edge geospatial technologies to monitor ecosystems and the development of tailored tools for assessing such natural areas is a fundamental task. In this context, the growing availability of hyperspectral (HS) imagery from satellite and aerial platforms can provide valuable information for the sustainable management of ecosystems. However, in some cases, the spectral richness provided by HS sensors is at the expense of spatial quality. To alleviate this inconvenience, which can be critical to monitor some heterogeneous and mixed natural areas, a number of HS sharpening techniques have been developed to increase the spatial resolution while trying to preserve the spectral content. This image processing field has attracted the interest of the scientific community, and many research studies have been conducted to assess the performance of different HS sharpening algorithms. In the last decade, however, many comparative studies rely upon simulated data. In this work, the challenging application of sharpening methods in real situations using multiplatform or multisensor data is also addressed. Thus, experiments with real data have been conducted, in addition to a thorough assessment of HS sharpening techniques using simulated imagery in scenarios with different spatial resolution ratios and registration errors. In particular, airborne and satellite HS imageries have been pansharpened with drone, orthophotos, and satellite high spatial resolution data evaluating 11 fusion algorithms. After a comprehensive analysis, considering different visual and quantitative quality indicators, the algorithm characteristics have been summarized and the methods with higher performance and robustness have been identified.