PAN High-resolution Image Fusion : Methods to Preserve Spectral and Spatial Resolution

The main topic of this paper is high-resolution image fusion. The techniques used to merge high spatial resolution panchromatic images with high spectral resolution multispectral images are described. The most commonly used image fusion methods that work on the principle of component substitution (intensity-hue-saturation method (IHS), Brovey transform (BT), and multiplicative method (MULTI)) have been applied to Ikonos, QuickBird, Landsat, and aerial orthophoto images. Visual comparison, histogram analyses, correlation coefficients, and difference images were used in order to analyze the spectral and spatial qualities of the fused images. It was discovered that for preserving spectral characteristics, one needs a high level of similarity between the panchromatic image and the respective multispectral intensity. In order to achieve this, spectral sensitivity of multispectral and panchromatic data was performed, and digital values in individual bands have been modified before fusion. It has also been determined that spatial resolution is best preserved in the event of an unchanged input panchromatic image. Introduction Earth observation satellites provide an increasing amount of data at different spatial, temporal, and spectral resolutions. In order to be able to (effectively) solve real world problems, advanced methods of data fusion are being developed. These methods integrate different data in order to obtain additional information then merely the data that can be derived from each of the sensors. During the last decades, data fusion has been a rapidly developing area of research in remote sensing. Several authors have recently documented new or improved methods and their applications. Pohl and van Genderen (1998) published an extensive review on image fusion techniques, listing approximately 150 references. Most of the newest remote sensing systems, such as Ikonos, QuickBird, IRS, SPOT, and Landsat 7, provide sensors with one high spatial resolution panchromatic (PAN) and several multispectral bands (MS). There are several reasons for not capturing the images merely in high resolution: the most important of them being the incoming radiation energy and the data volume collected by the sensor (Zhang, 2004). PAN High-resolution Image Fusion: Methods to Preserve Spectral and Spatial Resolution Andreja Švab and Krištof Oštir images cover a broader wavelength range and in order to receive the same amount of incoming energy, the size of a PAN detector can be smaller than that of a MS detector. Therefore, on the same satellite or airplane platform, the resolution of the PAN sensor can be higher than that of the MS sensor. On the other hand, the data volume of a high-resolution MS image would be significantly larger and could mitigate the problems of limited on-board storage capacity and limited data transmission rates between the platform and ground (Zhang, 2004). Since a number of applications need both high spectral and high spatial resolution, image fusion, or more precisely, band sharpening or resolution merge, is used. Image fusion is a method, which increases the spatial resolution of multispectral images (ideally without the loss of spectral information), through the combination of low spatial but high spectral resolution multispectral data and higher spatial but low spectral resolution panchromatic data. A short overview of the image fusion methods is given in the paper. However, only the three most commonly used methods that work on the principle of component substitution, i.e., the intensity-hue-saturation method (IHS), Brovey transform (BT), and the multiplicative method (MULTI), are treated in greater detail. They are tested with different combinations of Ikonos, QuickBird, Landsat, and aerial orthophoto images. Special attention is paid to the quality analysis of the results regarding the preservation of spectral and spatial resolution. Image Fusion Methods Numerous methods have been implemented to fuse multitemporal, multisensor, and multiresolution data. For a comprehensive review of the development one should refer to the recent overview papers in remote sensing (Pohl and van Genderen, 1998; Zhang, 2004), and related fields, such as signal processing (Li et al., 1995), or medical imaging (Barillot et al., 1993; Townsend and Cherry, 2001). In general, the image fusion techniques can be divided into two classes: color related techniques, and statistical or numerical methods. The first group comprises of the tristimulus color composition in the red, green, blue RGB color space as well as more sophisticated transformations (for example IHS). Statistical approaches use channel statistics including correlation (principal components analysis (PCA), regression), and filters (high pass), while numerical methods follow arithmetic operations such as image addition, division, PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING May 2006 565 Andreja Švab was formerly with the Igea, Koprska ulica 94, Ljubljana, Slovenia and is currently with the Municipality of Maribor, Ulica heroja Staneta 1, Maribor, Slovania (andreja. svab@maribor.si). Krištof Oštir is with the Scientific Research Centre of Slovenian Academy for Sciences and Arts, Novi trg 2, Ljubljana, Slovenia (kristof@zrc-sazu.si). Photogrammetric Engineering & Remote Sensing Vol. 72, No. 5, May 2006, pp. 565–572. 0099-1112/06/7205–0565/$3.00/0 © 2006 American Society for Photogrammetry and Remote Sensing HR-05-015.qxd 4/10/06 2:57 PM Page 565