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Improvement of remotely sensed low spatial resolution images by back-propagated neural networks using data fusion techniques

In this work an application of the back-propagated neural networks in the spatial resolution improvement of remotely sensed low resolution images using data fusion principles is described. Various tests are performed. In one study, the resolution of visible and near-infrared band images is enhanced using a panchromatic high resolution image and in the other, the resolution of the thermal band image is improved using other spectral band high resolution images. Different improvement rates are tested and the improvement covers spectral bands with wavelengths ranging from 0.45 μm to 2.35 μm and also the thermal band. The tests developed are explained and examples of the results obtained from each test are shown and compared with the correct patterns from which an error analysis is also carried out.