Evidence map›Paper›PMID 38203156›Full record

ArticleSensors (Basel, Switzerland)2024

Color Night Light Remote Sensing Images Generation Using Dual-Transformation.

Yanling Lu, Guoqing Zhou, Meiqi Huang, Yaqi Huang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yanling LuGuangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China.ORCID 0009-0007-7688-4868
Guoqing ZhouGuangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China.ORCID 0000-0001-8295-0496
Meiqi HuangCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China.
Yaqi HuangCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China.

Funding

BaGuiScholars program of Guangxi Guoqing ZhouGuangxi Innovative Development Grand Program GuikeAA18118038 and GuikeAA18242048Guangxi Natural Science Foundation for Innovation Research Team 2019GXNSFGA245001Guangxi Science and Technology Base and Talent Project Guike AD19254002National Key Research and Development Program of China 2016YFB0502501National Natural Science of China 41961065
6 · The paper itself

Abstract

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.

Indexed as

color night light remote sensing imagesIHS color space transformimage fusionmulti-source remote sensingwavelet transform

Identifiers

PMID38203156
PMCPMC10781353

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.