Automatic Image Registration Through Image Segmentation and SIFT

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Hernani Goncalves (Autor) FCUP; Luis Corte Real (Autor) FEUP; Jose A Goncalves (Autor) FCUP

Revista

Título: IEEE Transactions on Geoscience and Remote Sensing

Vol. 49 Nº 7

Páginas: 2589-2600

ISSN: 0196-2892

Editora: IEEE

Indexação

ISI Web of Knowledge - 176 Citações

ISI Web of Science

Scopus - 204 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrotécnica

Outras Informações

ID Authenticus: P-002-Q6T

DOI: 10.1109/tgrs.2011.2109389

Abstract (EN): Automatic image registration (AIR) is still a present challenge for the remote sensing community. Although a wide variety of AIR methods have been proposed in the last few years, there are several drawbacks which avoid their common use in practice. The recently proposed scale invariant feature transform (SIFT) approach has already revealed to be a powerful tool for the obtention of tie points in general image processing tasks, but it has a limited performance when directly applied to remote sensing images. In this paper, a new AIR method is proposed, based on the combination of image segmentation and SIFT, complemented by a robust procedure of outlier removal. This combination allows for an accurate obtention of tie points for a pair of remote sensing images, being a powerful scheme for AIR. Both synthetic and real data have been considered in this work for the evaluation of the proposed methodology, comprising medium and high spatial resolution images, and single-band, multispectral, and hyperspectral images. A set of measures which allow for an objective evaluation of the geometric correction process quality has been used. The proposed methodology allows for a fully automatic registration of pairs of remote sensing images, leading to a subpixel accuracy for the whole considered data set. Furthermore, it is able to account for differences in spectral content, rotation, scale, translation, different viewpoint, and change in illumination.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12