Remote-sensing image fusion based on Multiwavelet Transform and IHS Transform

Wang, Tianpeng; Li, Bicheng; Yu, Chaoqun; Cao, Wen

doi:10.1109/icosp.2006.345603

Cited by 3 publications

(2 citation statements)

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“…Liu et al (2018) combined multi-scale analysis to optimize the processing of different wavebands and proposed an improved Gram-Schmidt (GS) fusion algorithm based on the IHS transform [18]. Wang et al (2021) combined the advantages of three algorithms; namely, IHS, Principal Component Analysis (PCA), and wavelet transform, to minimize the spectral distortion caused by uncorrelated replacement components and resolve the detailed distortion of wavelet transform [19].…”

Section: Introductionmentioning

confidence: 99%

“…The NPP-VIIRS-like night light remote sensing datasets spanning from 2000 to 2020, which were derived from the new long time-series (2000-2020) night light datasets proposed and established by Yu's team with a spatial resolution of 500 m[9,19]; and the NPP-VIIRS-like data were extracted for the Shanghai study area. (2) The Landsat MS and PAN datasets, which had spatial resolutions of 30 and 15 m for Shuttle Radar Topography Mission (SRTM) V3 data.…”

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confidence: 99%

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Color Night Light Remote Sensing Images Generation Using Dual-Transformation

Lu,

Zhou,

Huang

et al. 2024

Sensors

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Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and “NPP-VIIRS-like” night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%