A Process to Minimize the Spectral Distortion in 1m IKONOS Color Image by Using Four Parameters

Kudoh, J.; Kalpoma, KaziA.; Kurita, Y.

doi:10.1109/igarss.2006.832

Cited by 5 publications

References 2 publications

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An IKONOS 1m Color Image Fusion Processing with Wavelet Transform

Kudoh

Kalpoma

Kurita

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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For image fusion applications, the critical issue is how to produce a fused imagery with minimizing color distortion and maximizing the crease. However, due to nonsymmetrical spectral responses of IKONOS imagery, most existing techniques produce color distortion in 1m fused images. In our another report we have succeeded to reduce this color distortion significantly by using four spectral weighting parameters in our proposed fusion technique [1]. However, block distortion appeared in part of the 1m fused image. In this work we investigated the causes and proposed a sharpening process which uses Wavelet Transform to minimize this block distortion in 1m fused MS image. We have compared the 1m fused image before and after this sharpening process and found that the block distortion has almost been disappeared. Moreover, distortion of color of the entire image did not changed after this process.

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An IKONOS 1m Color Image Fusion Processing with Wavelet Transform

Kudoh

Kalpoma

Kurita

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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Image Fusion Processing for IKONOS 1-m Color Imagery

Kalpoma

Kudoh

2007

IEEE Trans. Geosci. Remote Sensing

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Adaptive steepest descent method for pan-sharpening of multispectral images

Liu

Wang

2011

Opt. Eng

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Many pan-sharpening techniques have been developed to synthesize a multispectral (MS) image at high resolution by fusing MS images and panchromatic (Pan) images. Most existing pan-sharpening methods can achieve results with high spatial resolution, but the spectral distortion in the fused results is still a problem that needs to be solved. In this paper, an adaptive linear model is proposed to reduce the spectral distortion by weakening the dependence on the correlation between Pan and MS. The difference between a Pan image and the combination of MS images is estimated by least square optimization, and embedded into the proposed model as a virtual band. According to the adaptive model, an iterative pan-sharpening algorithm is proposed based on the steepest descent method, in which the virtual band is used as a local adaptive constraint to the optimized solution. The proposed method is tested on datasets acquired by IKONOS, QuickBird, and Landsat 7 ETM + and compared with the existing methods. The quality measures and the visual impressions show that the proposed method is an efficient approach to preserving spectral information and represents strong robustness against various scenes and sensors. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). IntroductionTo maintain a satisfactory signal-to-noise ratio, there is a trade-off relationship between spectral and spatial resolution in the design of spectral imaging sensors. 1 Increasing the spatial resolution of multispectral (MS) imaging sensors is an expensive proposition. Most optical remote sensing satellites, such as QuickBird, SPOT, IKONOS, and Landsat 7 provide two types of data: MS images at low spatial resolution and panchromatic (Pan) images at high spatial resolution. The high-resolution MS images are demanded in various applications, such as land use, vegetation, and urban studies. 2, 3 Pan-sharpening techniques provide the efficient and economical approaches to generate MS with high spatial resolution by fusing the spectral information in MS and the spatial information in Pan.The existing pan-sharpening methods can be classified into three categories: component substitution methods, spectral contribution methods, and multiscale analysis-based methods. 4 All of them are developed based on the assumption of a relationship between Pan and MS images. For the former two kinds of methods, the assumptions are the same, that a Pan image is the linear combination of the corresponding MS images. The performance of these methods, especially in terms of spectral quality, depends on the correlation between Pan and MS images. 4 Multiscale analysis-based methods 5-7 were summarized as ARSIS (from its French acronym, which means improving spatial resolution by structure injection) concept by Ranchin and Wald. 5 The fundamental assumption of ARSIS methods is that the missing spatial information in MS is contained in the high frequency modality of

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A Process to Minimize the Spectral Distortion in 1m IKONOS Color Image by Using Four Parameters

Cited by 5 publications

References 2 publications

An IKONOS 1m Color Image Fusion Processing with Wavelet Transform

An IKONOS 1m Color Image Fusion Processing with Wavelet Transform

Image Fusion Processing for IKONOS 1-m Color Imagery

Adaptive steepest descent method for pan-sharpening of multispectral images

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