THE USE OF SIMILARITY IMAGES ON MULTI-SENSOR AUTOMATIC IMAGE REGISTRATION

• Autor(a) principal: Goncalves, H
• Data de Publicação: 2010
• Outros Autores: Goncalves, JA, Corte Real, L
• Tipo de documento: Livro
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/52956
• topic: Engenharia do ambiente; Environmental engineering

Automatic image registration (AIR) is still a present challenge regarding remote sensing applications. Although several methods have been proposed in the last few years, geometric correction is often a time and effort consuming manual task. The only AIR method which is commonly used is the correlation-based template matching method. It usually consists on considering a window from one image and passing it throughout the other, looking for a maximum of correlation, which may be associated to the displacement between the two images. This approach leads sometimes (for example with multi-sensor image registration) to low correlation coefficient values, which do not give sufficient confidence to associate the peak of correlation to the correct displacement between the images. Furthermore, the peak of correlation is several times too flat or ambiguous, since more than one local peak may occur. Recently, we have tested a new approach, which shortly consists on the identification of a brighter diagonal on a "similarity image". The displacement of this brighter diagonal to the main diagonal corresponds to the displacement in each axis. In this work, we explored the potential of using the "similarity images" instead of the classical "similarity surface", considering both correlation coefficient and mutual information measures. Our experiments were performed on some multi-sensor pairs of images with medium (Landsat and ASTER) and high (IKONOS, ALOS-PRISM and orthophotos) spatial resolution, where a subpixel accuracy was mostly obtained. It was also shown that the application of a low-pass filtering prior to the similarity measures computation, allows for a significant increase of the similarity measures, reinforcing the strength of this methodology in multi-spectral, multi-sensor and multi-temporal situations.