• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1633-1649
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• 2017

In remote sensing image processing, the traditional fusion algorithm is based on the Intensity-Hue-Saturation (IHS) transformation. This method does not take into account the texture or spectrum information, spatial resolution and statistical information of the photos adequately, which leads to spectrum distortion of the image. Although traditional solutions in such application combine manifold methods, the fusion procedure is rather complicated and not suitable for practical operation. In this paper, an improved IHS transformation fusion algorithm based on the local variance weighting scheme is proposed for remote sensing images. In our proposal, firstly, the local variance of the SPOT (which comes from French "Systeme Probatoire d'Observation dela Tarre" and means "earth observing system") image is calculated by using different sliding windows. The optimal window size is then selected with the images being normalized with the optimal window local variance. Secondly, the power exponent is chosen as the mapping function, and the local variance is used to obtain the weight of the I component and match SPOT images. Then we obtain the I' component with the weight, the I component and the matched SPOT images. Finally, the final fusion image is obtained by the inverse Intensity-Hue-Saturation transformation of the I', H and S components. The proposed algorithm has been tested and compared with some other image fusion methods well known in the literature. Simulation result indicates that the proposed algorithm could obtain a superior fused image based on quantitative fusion evaluation indices.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.71-76
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• 2001

인공위성을 이용하여 산불피해지역을 분석하기 위해 KT(Kauth-Thomas)변환기법과 IHS(Intensity-Hue-Saturation)변환기법을 적용하여 비교해 보고 산불피해등급지도를 작성하였다. 방사보정과 지형보정을 수행한 영상을 각각 IHS와 KT로 변환시킨 후 최대우도법에 의하여 분류하였다. 정확도 평가에서 KHAT statistic은 각각 0.67와 0.76을 나타내었다. 현장데이터가 부족하여 cross-validation을 수행하였으며, 일관되게 KT변환기법에 의한 분류결과가 IHS기법에 의한 분류결과보다 더 높은 정확도를 보여주었다. 또한 KT feature space 와 IHS 컴포넌트의 분광 분포를 그래프 상에서 분석해 보았다. 3개의 KT feature 중, greenness와 wetness가 brightness 보다 각 클래스에 대해서 보다 높은 분리성을 제공하였다. 하니만 IHS 컴포넌트의 분광분포는 뚜렷한 분리성이 나타나지 않고 서로 섞여 있는 것을 볼 수 있었다. 따라서, KT변환기법이 IHS변환기법보다 산불피해지역을 추출함에 있어 더 높은 정확도를 나타내고, 산불과 관련된 지표의 물리적 특성을 더 잘 반영한다고 할 수 있다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.13 no.2
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• pp.163-168
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• 1995

There are four types of color model to repesent color, which are RGB, IHS, CMY, and YIQ color model. RGB color model is the designation of the digital numbers(DNs) of the three primary colors(red, green, and blue), which are used to produce color images on color monitors. IHS color model is the designation of in-tensity, hue, and saturation(IHS). An advantage of considering color in terms of IHS over that of RGB is arrives more easily at a desired color product mathematically. In this study, authors use the IHS transformation and in-tensity-adjustment method to produce the color map images with Landsat TM and scanned map image. And, authors suggest the problems and their solutions when users produce the desired new images with satellite images and map images.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.125-135
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• 2009

Owing to its fast computing capability for fusing images, the FIHS(Fast Intensity Hue Saturation) fusion is widely used for fusion purposes. However, the FIHS fusion also distorts color in the same way such as the IHS(Intensity Hue Saturation) fusion technique. In this paper, a FIHS fusion technique(FIHS-BR) which reduces color distortion by using the ratio of each spectral band and an adaptive FIHS fusion(FIHS-SABR) using spatial information and the ratio of each spectral band are proposed. The proposed FIHS-BR fusion reduces color distortion by adding different spatial detail improvement values for each spectral band. The spatial detail improvement values are derived from the ratio of spectral band. And the proposed FIHS-SABR fusion reduces more color distortion by readjusting the spatial detail improvement values for each spectral band according to the ratio of the spectral bands. The spatial detail improvement values are derived adaptively from the characteristics of spatial information of the local image. To evaluate the performance of the proposed FIHS-BR fusion and FIHS-SABR fusion, a computer simulation is performed for IKONOS remote sensing image. Results from the experiments show that the proposed methods have less color distortion for the forest regions which reveal severe color distortion in the traditional FIHS fusion. From the evaluation results of the characteristics of spectral information for fused image, we show that the proposed methods have best results.

• Korean Journal of Geomatics
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• v.3 no.1
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• pp.59-65
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• 2003

The merging of different scales or multi-sensor image data is becoming a widely used procedure of the complementary nature of various data sets. Ideally, the merging method should not distort the characteristics of the high-spatial and high-spectral resolution data used. To present an effective merging method for survey of reclaimed land using the high-resolution (6.6 m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSA T-l) and the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data, this paper compares the results of Intensity Hue Saturation (IHS) and Principal Component Analysis (PCA) methods. The comparison is made by statistical and visual evaluation of three-color combination images of IHS and PCA results based on spatial and spectral characteristics. The use of MODIS bands 1, 2, and 3 with a contrast stretched EOC panchromatic image as a substitute for intensity was found to be particularly effective in this study.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.171-180
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• 2003

The merging of different scales or multi-sensor image data is becoming a widely used procedure of the complementary nature of various data sets. Ideally, the merging method should not distort the characteristics of the high-spatial and high-spectral resolution data used. To present an effective merging method for survey of reclaimed land, this paper compares the results of Intensity Hue Saturation (IHS), Principal Component Analysis (PCA), Color Normalized(CN) and High Pass Filter(HPF) methods used to merge the information contents of the high-resolution (6.6 m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSAT-1) and the multi-spectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data. The comparison is made by visual evaluation of three-color combination images of IHS, PCA, CN and HPF results based on spatial and spectral characteristics. The use of a contrasted EOC panchromatic image as a substitute for intensity in merged images with MODIS bands 1, 2 and 3 was found to be particularly effective in this study.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.39-54
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• 2012

The objective of this study is to propose efficient data fusion techniques feasible to the KOMPSAT-2 satellite images. The most widely used image fusion techniques, which are the high-pass filter (HPF), the intensity-hue-saturation-based (modified IHS), the pan-sharpened, and the wavelet-based methods, was applied to four KOMPSAT - 2 satellite images having different regional and seasonal characteristics. Each fusion result was compared and analyzed in spatial and spectral features, respectively. Quality evaluation of image fusion techniques was performed in both quantitative and visual analysis. The quantitative analysis methods used for this study were the relative global dimensional error (spatial and spectral ERGAS), the spectral angle mapper index (SAM), and the image quality index (Q4). The results of quantitative and visual analysis indicate that the pan-sharpened method among the fusion methods used for this study relatively has the suitable balance between spectral and spatial information. In the case of the modified IHS method, the spatial information is well preserved, while the spectral information is distorted. And also the HPF and wavelet methods do not preserve the spectral information but the spatial information.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.149-164
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• 1998

The main object of this study was to prove the effectiveness of different merging methods by using the high resolution IRS(Indian Remote Sensing Satellite)-1C panchromatic data and the multispectral Landsat TM data. The five methods used to merging the information contents of each of the satellite data were the intensity-hue-saturation(IHS), principal component analysis(PCA), high pass filter(HPF), ratio enhancement method and look-up-table(LUT) procedures. Two measures are used to evaluate the merging method. These measures include visual inspection and comparisons of the mean, standard deviation and root mean square error between merged image and original image data values of each band. The ratio enhancement method was well preserved the spectral characteristics of the data. From visual inspection, PCA method provide the best result, HPF next, ratio enhancement, IHS and LUT method the worst for the preservation of spatial resolution.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.902-904
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• 2003

The main objective of this paper is to investigate the potential utility of multisensor remotely sensed data for improved coastal wetland mapping. Five data fusion models, three algebraic models (Multiplicative (MT), Brovey (BT) and Wavelet transform (WT)) and two spectral domain models (Principals component transform (PCT) and Intensity-Hue-Saturation (IHS)) were implemented and tested over the multisensor data. The fused images were then compared based on visual and statistical approaches. The results show that the wavelet transform provides greater flexibility for combining optical data sets and has good potential for preserving the spatial and spectral content of the original images . However, this model yields poor information when combining optical and microwave data. Brovey transform is more reliable for fusing optical and microwave image data and yields improved information about different wetland features of the coastal zone.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.359-367
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• 2011

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.