Assessment of very high resolution satellite data fusion techniques for landslide recognition

Pan-sharpening is gaining an increasing attention in the remote sensing community, and its usefulness have been demonstrated in several environmental applications. A variety of pan-sharpening techniques, aiming at improving the quality of the fused image have been proposed in literature, but the ranking of their efficiency is a difficult task since the quality of the pan-sharpened image depends on the considered applications. In the literature the IHS-based technique has been proposed as the most effective for landslide detection, but in a more generic framework, other methods such as the Gram-Schmidt Adaptive (GSA) and the General Laplacian Pyramid (GLP) have been found as most performing than the IHS, together with their improved Context Adaptive versions, the GSA-CA and GLP-CA, that relies on local statistics. In the context of the MORFEO project, funded by the Italian Spatial Agency (ASI), this work aims at verifying these conclusions by comparing the performances of IHS, GSG and GSA-CA methods together with those of the Principal Component (PC) and the widely used Gram Schmidt (GS) methods. The comparison have been performed on IKONOS multispectral data, by evaluating the results both in a quantitative and qualitative way. The qualitative assessment has been performed by means of a visual assessment in terms of landslide detection by photointerpretative techniques. Possible correlation and or differences found among the quantitative and the visual assessment have been analyzed.