Image Fusion for Remote Sensing Applications

Citations Over TimeTop 10% of 2011 papers

Abstract

Remote Sensing systems, particularly those deployed on satellites, provide a repetitive and consistent view of the Earth (Schowengerdt, 2007). To meet the needs of different remote sensing applications the systems offer a wide range of spatial, spectral, radiometric and temporal resolutions. Satellites usually take several images from frequency bands in the visual and non-visual range. Each monochrome image is referred to as a band and a collection of several bands of the same scene acquired by a sensor is called multispectral image (MS). A combination of three bands associated in a RGB (Red, Green, Blue) color system produce a color image. The color information in a remote sensing image by using spectral band combinations for a given spatial resolution increases information content which is used in many remote sensing applications. Otherwise, different targets in a single band may appear similar which makes difficult to distinguish them. Different bands can be acquired by a single multispectral sensor or by multiple sensors operating at different frequencies. Complementary information about the same scene can be available in the following cases (Simone et al., 2002):  Data recorded by different sensors;  Data recorded by the same sensor operating in different spectral bands;  Data recorded by the same sensor at different polarization;  Data recorded by the same sensor located on platforms flying at different heights. In general, sensors with high spectral resolution, characterized by capturing the radiance from different land covers in a large number of bands of the electromagnetic spectrum, do not have an optimal spatial resolution, that may be inadequate to a specific identification task despite of its good spectral resolution (Gonzalez-Audicana, 2004). On a high spatial resolution panchromatic image (PAN), detailed geometric features can easily be recognized, while the multispectral images contain richer spectral information. The capabilities of the images can be enhanced if the advantages of both high spatial and spectral resolution can be integrated into one single image. The detailed features of such an integrated image thus can be easily recognized and will benefit many applications, such as urban and environmental studies (Shi et al., 2005).

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