A Fast Intensity–Hue–Saturation Fusion Technique With Spectral Adjustment for IKONOS Imagery

Tu, Te-Ming; Huang, Ping; Hung, Chung-Ling; Chang, Chien-Ping

doi:10.1109/lgrs.2004.834804

Cited by 537 publications

(324 citation statements)

References 3 publications

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“…A simple and fast procedure is based on the intensity-hue-saturation (IHS) transform [4], which can be used only when three bands are available. However, a generalized IHS transform (GIHS) can be defined [5] which includes the response of the near-infrared (NIR) band. Other methods use the principal component analysis (PCA) [6], the Brovey transform (BT) [7], and Gram-Schmidt (GS) spectra sharpening [8].…”

Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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Abstract:A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

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Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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“…Considering different characteristics between the TIR band and the visible bands, traditional image fusion models, such as the widely used Principle Component Analysis (PCA) based fusion methods [23,24], Intensity-Hue-Saturation (HIS) transformation method [25,26] and wavelet-based image fusion methods [27,28], are more concentrating on the visual effects of the fused images, which may not be useful for quantitative remote sensing applications. Hence, some downscaling approaches have been developed for TIR images.…”

Section: Introductionmentioning

confidence: 99%

Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

et al. 2015

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As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST) retrieved from Thermal Infra-Red (TIR) images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial-and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM) algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) in order to derive high resolution synthetic data. The synthetic images were OPEN ACCESSRemote Sens. 2015, 7 4425 evaluated for both testing and simulated satellite images. The average difference (AD) and absolute average difference (AAD) are smaller than 1.7 K, where the correlation coefficient (CC) and root-mean-square error (RMSE) are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.

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“…Consequently, their application should be limited to sensors designed with a panchromatic canal that overlaps the whole spectral range of all the MS bands used in the three-band composite. Digressions in the spectral domain may otherwise result in significant radiometric distortions [7]. Therefore, color space substitution methods are irrelevant to MODIS, because the HR bands are not panchromatic and do not overlap spectrally with the LR bands.…”

Section: A Image Fusion 1) Principle and Methodsmentioning

confidence: 99%

Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept

Sirguey

Mathieu

Arnaud

et al. 2008

IEEE Geosci. Remote Sensing Lett.

102

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Abstract-We propose to fuse the high spatial content of two 250-m spectral bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) into its five 500-m bands using wavelet-based multiresolution analysis. Our objective was to test the effectiveness of this technique to increase the accuracy of snow mapping in mountainous environments. To assess the performance of this approach, we took advantage of the simultaneity between the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS sensors. With a 15-m spatial resolution, the ASTER sensor provided reference snow maps, which were then compared to MODIS-derived snow maps. The benefit of the method was assessed through the investigation of various metrics, which showed an improvement from 3% to 20%. Therefore, the enhanced snow map is of great benefit for environmental and hydrological applications in steep terrain.

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A Fast Intensity–Hue–Saturation Fusion Technique With Spectral Adjustment for IKONOS Imagery

Cited by 537 publications

References 3 publications

Pansharpening by Convolutional Neural Networks

Pansharpening by Convolutional Neural Networks

Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept

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