Optimizing the High-Pass Filter Addition Technique for Image Fusion

نویسندگان

  • Ute G. Gangkofner
  • Pushkar S. Pradhan
چکیده

Pixel-level image fusion combines complementary image data, most commonly low spectral-high spatial resolution data with high spectral-low spatial resolution optical data. The presented study aims at refining and improving the High-Pass Filter Additive (HPFA) fusion method towards a tunable and versatile, yet standardized image fusion tool. HPFA is an image fusion method in the spatial domain, which inserts structural and textural details of the higher resolution image into the lower resolution image, whose spectral properties are thereby largely retained. Using various input image pairs, workable sets of HPFA parameters have been derived with regard to high-pass filter properties and injection weights. Improvements are the standardization of the HPFA parameters over a wide range of image resolution ratios and the controlled trade-off between resulting image sharpness and spectral properties. The results are evaluated visually and by spectral and spatial metrics in comparison with wavelet-based image fusion results as a benchmark. Introduction Pixel-based image fusion techniques amalgamate the physical properties of the source images into a new image. The most frequent application for image fusion is what many authors call resolution merging or pan-sharpening, where multispectral data of coarser spatial resolution are merged with data of finer spatial resolution, usually panchromatic (PAN) imagery. The result is an artificial multispectral image with the spatial resolution of the panchromatic image (Steinnocher 1999). The perfect pan-sharpening result would be identical to the image the multispectral sensor would have observed if it had the spatial resolution of the panchromatic sensor (Wald et al., 1997). That means the goal of pan-sharpening is to render a sharpened image incorporating the full spatial information content of the panchromatic image without introducing spectral distortions to the multispectral input data. Not only is spectral fidelity of the fusion result important for visual image interpretation, it is also essential for automatic image classification approaches (Pradhan et al., 2006). Optimizing the High-Pass Filter Addition Technique for Image Fusion Ute G. Gangkofner, Pushkar S. Pradhan, and Derrold W. Holcomb Several currently operating optical sensors (e.g., SPOT, Landsat, QuickBird, Ikonos, etc.) generate such a combination of higher resolution panchromatic and lower resolution multispectral data. Because they are limited by the data transmission rates, the multispectral bands are recorded in a lower resolution and only the panchromatic band is transmitted at its full resolution (Pradhan, 2005). In addition, pan-sharpening can also be applied to images from different sensors, which widens the scope (i.e., ground resolution ratios, R) and consequently the methodological requirements for such image fusions. Within multi-sensor image fusion of complementary sensors, a focus can be observed on the fusion of radar data and optical data. Radar/optical image fusion can be used for image enhancement purposes (Garzelli, 2002) as well as to “fill in” missing image parts obscured by clouds in optical data (Arellano, 2003), or as a sort of resolution merge (Mercer et al., 2005). Short Overview of Existing Image Fusion Approaches Image fusion procedures are often subdivided according to the abstraction level of the fusion, where pixel-based, featurebased, and decision-based approaches are distinguished (Pohl and Van Genderen, 1998). Within pixel-based image fusion, a rough distinction can be made between the commonly applied spectral substitution techniques (IntensityHue-Saturation (IHS), Principal Components (PC)), arithmetic merging (e.g., multiplicative merging, Brovey transform), and methods in the spatial domain (e.g., wavelet, HPFA). Spectral substitution techniques have been widely discussed in the literature with varying, partly contradictory, results. Lemeshewsky (2002) discusses some theoretical limitations of IHS sharpening and suggests that sharpening of the bands individually may be preferable. Yocky (1995) demonstrates that the IHS transform can distort colors (particularly red) and discusses theoretical explanations. Nunez et al. (1999) compare three IHS calculations and settle for the intensity on I (R G B)/3 as the most appropriate. While the IHS method is limited to three input bands, the PC method will accept any number of input data layers. Lemeshewsky (2002) suggested that this technique produces an output image that better preserves the numerical integrity of the input dataset than the IHS method. Zhang (1999), however, has reached the opposite conclusion regarding the PC versus IHS approaches. Among arithmetic merging methods, a variety of approaches exist involving different arithmetic operations PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Sep t embe r 2008 1107 U.G. Gangkofner is with GeoVille Information Systems GmbH, Museumstr. 9–11, A 6020 Innsbruck, Austria ([email protected]). P.S. Pradhan is with Photondynamics Inc. 5970 Optical Court, San Jose, CA, and formerly with the Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39759. D.W. Holcomb is with Leica Geosystems, Atlanta, GA 30329. Photogrammetric Engineering & Remote Sensing Vol. 74, No. 9, September 2008, pp. 1107–1118. 0099-1112/08/7409–1107/$3.00/0 © 2008 American Society for Photogrammetry and Remote Sensing 06-037.qxd 8/9/08 12:27 AM Page 1107

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تاریخ انتشار 2008