TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm

Teggi, Sergio; Cecchi, Rodolfo; Serafini, Frank

doi:10.1080/01431160210144561

Cited by 57 publications

(24 citation statements)

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“…The frequency responses of the equivalent 1-D analysis filters are shown in Fig. 1(a) and (b), for two different lowpass filters h: the 23-taps GLP filter [9] and the Gaussian-like filter derived by Starck and Murtagh [23] from a cubic spline scaling function and widely used for image fusion [11,24,25]. Apart from the lowpass filter (leftmost), all the other filters are bandpass with bandwidths roughly halved as j increases by one.…”

Section: ''à Trous'' Wavelet Transformmentioning

confidence: 99%

Remote sensing image fusion using the curvelet transform

et al. 2007

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Section: ''à Trous'' Wavelet Transformmentioning

confidence: 99%

Remote sensing image fusion using the curvelet transform

et al. 2007

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“…The first PC contains more information since it has a large percentage of all the variance. Generally, %95 of the total variance is contained in the first three principal components (POHL & GENDEREN, 1998;TEGGI et al, 2003). The difference between IHS and PCA is that while IHS is used for 3 bands, PCA method can be used for more than three bands.…”

Section: Image Fusionmentioning

confidence: 99%

“…At present, IHS (LI and WANG, 2001;TSAI, 2004), Brovey Transformation (BINH et al, 2006), PCA (AMARSAIKHAN & DOUGLAS, 2004), HPF (BETHUNE et al, 1998;AIAZZI et al, 2006a), DWT (ZHANG & HONG 2005;JIN et al, 2006), Gram Schmidt Transformation (AIAZZI et al, 2006b), Smoothing Filter Based Intensity Modulation (LIU, 2000), Synthetic Variable Ratio (ZHANG, 1999) are among the pixel based image fusion algorithms generally used. In the literature, there are several studies comparing these techniques analyzed by Zhou et al (1998), Zhang and Hong (2005), Colditz et al (2006), Teggi et al (2003), Shi et al (2005), Wang and Bovik (2002). Fusion methods are used for gathering information from SAR and Optical data for various purposes such as qualifying the fused images (ROKHMATULOH et al, 2003;SHI et al, 2005) for enhancement of the geological information gathered (Pal et al, 2007), and assessing the contribution of fusion techniques to rural and urban land use classification (AMARSAIKHAN et al, 2012;KURUCU et al, 2009;KUPLICH et al, 2000;SUN et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Comparison of different fusion algorithms in urban and agricultural areas using sar (palsar and radarsat) and optical (spot) images

Abdikan

Şanlı

2012

Bol. Ciênc. Geod.

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Image fusion techniques of remote sensing data are formal frameworks for merging and using images originating from different sources. This research investigates the quality assessment of Synthetic Aperture Radar (SAR) data fusion with optical imagery. Two different SAR data from different sensors namely RADARSAT-1 and PALSAR were fused with SPOT-2 data. Both SAR data have the same resolutions and polarisations; however images were gathered in different frequencies as C band and L band respectively. This paper contributes to the comparative evaluation of fused data for understanding the performance of implemented image fusion algorithms such as Ehlers, IHS (Intensity-Hue-Saturation), HPF (High Pass Frequency), two dimensional DWT (Discrete Wavelet Transformation), and PCA (Principal Component Analysis) techniques. Quality assessments of fused images were performed both qualitatively and quantitatively. For the statistical analysis; bias, correlation coefficient (CC), difference in variance (DIV), standard deviation difference (SDD), universal image quality index (UIQI) methods were applied on the fused images. The evaluations were performed by categorizing the test area into two as "urban" and "agricultural". It has been observed that some of the methods have enhanced either the spatial quality or preserved spectral quality of the original SPOT XS image to various degrees while some approaches have introduced distortions. In general we noted that Ehlers' spectral quality is far better than thoseComparison of diferent fusion algorithms in urban and agricultural... Bol. Ciênc. Geod., sec. Artigos, Curitiba, v. 18, n o 4, p. 509-531, out-dez, 2012.of the other methods. HPF performs almost best in agricultural areas for both SAR images. Keywords: Fusion; PALSAR; RADARSAT-1; SPOT; Statistical Analysis. RESUMO As técnicas de fusão de dados de sensoriamento remoto são estruturas formais para combinar e utilizar utilizar imagens originadas de diferentes fontes. Esta pesquisa investiga o critério de qualidade do SAR -Synthetic Aperture Radar -na fusão de dados com imagens óticas. Dois diferentes conjuntos de dados SAR originados de diferentes sensores chamados de RADARSAT-I e PALSAR -foram "fundidos " com dados SPOT-2. Ambos os dados de SAR têm a mesma resolução e polarização; no entanto, imagens foram agrupadas em diferentes freqüências tais como banda C e banda L, respectivamente. Esta pesquisa contribui para a avaliação comparativa de dados fundidos para o entendimento do desempenho do algoritmos de fusão de imagens implementadas tais como Ehlers, IHS ( Intensidade-do Matiz-de Saturação), HPF (Passagem de Alta Freqüência) , bidimensionais DWT ( Transformação Discreta de Ondaletas) e técnicas PCA (Análise de Componentes Principais). As avaliações de qualidade das imagens fundidas foram feitas qualitativamente e quantitativamente. Para a análise estatística; tendência, o Coeficiente de Correlação (CC), Diferença de Variância (DIV), Diferença do Desvio Padrão (SDD e, o Indice Universal de Qualidade da Imagem (UIQ...

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“…Combining these images, fusion produces a new multispectral image with a spatial resolution equal to the panchromatic one. In addition, image fusion introduces important distortion on the pixel spectra which in turn improve the information content of remote sensing (RS) images (Teggi et al 2003). Over the years, different fusion methods have been developed for improving spatial and spectral resolutions of RS data sets.…”

Section: Introductionmentioning

confidence: 99%

“…For several decades, fusion of multiresolution optical images has been successfully used for the improvement of information contents of images for visual interpretation as well as for the enhancement of land surface features. Many studies have been conducted on the improvement of spatial resolution of multispectral images by the use of the high frequencies of panchromatic images, while preserving the spectral information (Mascarenhas et al 1996, Saraf 1999, Teoh et al 2001, Teggi et al 2003, Gonzalez et al 2004, Colditz et al 2006, Deng et al 2008, Li and Leung 2009. A number of authors have attempted to successfully fuse the interferometric or multifrequency SAR images (Soh and Tsatsoulis 1999, Verbyla 2001, Baghdadi et al 2002, Costa 2005, Palubinskas and Datcu 2008.…”

Section: Introductionmentioning

confidence: 99%