Comparison of different pan-sharpening methods for spectral characteristic preservation: multi-temporal CARTOSAT-1 and IRS-P6 LISS-IV imagery

Abstract

A host of remote-sensing and mapping applications require both high spatial and high spectral resolutions. Availability of high spatial and spectral details at different resolutions from a suite of satellite sensors has necessitated the development of effective image fusion techniques that can effectively combine the information available from different sensors and take advantage of their varied capabilities. A common problem observed in the case of multi-sensor multi-temporal data fusion is spectral distortion of the fused images. Performance of a technique also varies with variation in scene characteristics. In this article, two sets of multi-temporal CARTOSAT-1 and Indian Remote Sensing satellite (IRS-P6) Linear Imaging and Self Scanning sensor (LISS-IV) image sub-scenes, with different urban landscape characteristics, are fused with an aim to evaluate the performance of five image fusion algorithms -high-pass filtering (HPF), Gram-Schmidt (GS), Ehlers, PANSHARP and colour-normalized Brovey (CN-Brovey). The resultant fused data sets are compared qualitatively and quantitatively with respect to spectral fidelity. Spatial enhancement is assessed visually. The difference in the performance of techniques with variation in scene characteristics is also examined. For both scenes, GS, HPF and PANSHARP fusion techniques produced comparable results with high spectral quality and spatial enhancement. For these three methods, the variation in performance over different scenes was not very significant. The Ehlers method resulted in spatially degraded images with a more or less constant negative offset in data values in all bands of one scene and in the first two bands in the other. The CN-Brovey method produced excellent spatial enhancement but highly distorted radiometry for both sub-scenes.

Effects of image pansharpening on soil total nitrogen prediction models in South India

“…Ghosh and Joshi (2013) utilized 12 fusion techniques to pansharpen WorldView-2 images, and the quantitative analysis indicated that high-pass filter, PANSHARP, GS and PCA method are the most efficient in increasing spatial resolution, while Brovey and Ehlers methods produced images with significant color distortion. Most research proved the advantages of GS method over IHS and Brovey method in terms of spectral quality preservation, spatial resolution enhancement and color distortion (Jalan and Sokhi, 2012;Yusuf et al, 2013).…”

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

“…Many research also compared different image pan-sharpened methods and their performance. Jalan and Sokhi (2012) showed high-pass filtering (HPF), Gram-Schmidt (GS) and PANSHARP methods produced comparable pan-sharpening images with high spectral quality and spatial enhancement, while Brovey method produced the pan-sharpening images with spatial enhancement but highly distorted radiometry. Karathanassi et al (2007) compared the 17 image pan-sharpening methods, local mean and variance matching (LMVM), least square fusion (LSF), and GS fusion methods have the highest performance in terms of peak signal-to-noise ratio (PSNR) and photointerpretation results.…”

Variational model-based very high spatial resolution remote sensing image fusion

“…Because each kind of imaging sensor can only focus on a given different operating spectral range and environmental conditions, it may not receive all the information necessary for detecting an object by human or computer vision. [16][17][18][19][20] Generally speaking, the methods producing high spatial quality may cause spectral distortion in the fused image. Fused images may provide improved interpretation capabilities and more reliable results since data with different characteristics are combined.…”