Fusion of Multispectral Images by Extension of the Pan-Sharpening ARSIS Method

Abstract

Some remote sensing applications, like coastal zone monitoring, require images having at the same time a high spatial, spectral, and temporal resolution. However, for the moment, no sensor provides these three characteristics at once. The medium resolution imaging spectrometer (MERIS) sensor combines high spectral resolution (15 bands) and low spatial resolution (300 m), whereas the enhanced thematic mapper (ETM) sensor combines reverse characteristics. The main objective of this work is to extend the pan-sharpening ARSIS method to the fusion of two multispectral images and to compare it with two other existing methods: the couple non-negative matrix factorization (CNMF) and a multisensor and multiresolution technique (). We then apply these three different methods to two sets of MERIS and ETM data: a synthetic set created from a Hyperion image in order to provide the reference image that would be acquired by a perfect sensor for validation, and a real set composed by MERIS and ETM co-registrated images. The results showed that the ARSIS method extended to the fusion of two multispectral images provides better statistical and visual results than the two other methods, on both synthetic and real datasets, and it is better adapted to water than to land applications.