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Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 24504

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Special Issue Editors

Prof. Dr. Andrea Garzelli

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Dept. Information Engineering and Mathematics, University of Siena, Via Roma, 56, I-53100 Siena, Italy
Interests: remote sensing; image fusion
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Dr. Simone Lolli

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CNR-IMAA, Contrada S. Loja, 85050 Tito Scalo, PZ, Italy
Interests: radiative transfer; aerosols; Lidar; image fusion; clouds; precipitations; aerosol–cloud interaction
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Prof. Dr. Kai Qin

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School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: atmospheric environment remote sensing; remote sensing information analysis; carbon monitoring
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Prof. Dr. Yuanjian Yang

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School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: multispectral remote sensing; cloud precipitation; land surface temperature; Doppler lidar; urban boundary layer; data fusion; artificial intelligence and weather prediction
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Special Issue Information

Dear Colleagues,

The availability of remote sensing data with different acquisition processes, spectral diversity (visible, near infrared, short wave infrared, thermal infrared, X- and C-band microwaves with related polarizations), and complementary spectral–spatial resolution,have promoted the development of many different data fusion techniques. This Special Issue is intended to present the latest advances in the field of multisensor data fusion from both active and passive remote sensing data products. The Special Issue will cover, among others, the following topics:

Multisensor data fusion from optical multispectral scanners, SAR, and LiDAR;
Spatial resolution enhancement for geometrically accurate applications in remote sensing and photogrammetry;
Spatiotemporal image fusion;
Registration of multisensor imagery;
New developments in estimation theory and machine learning for data fusion;
Application-oriented data fusion for classification, change detection, and biophysical parameter estimation;
Synergies between satellite and ground-based active remote sensing observations, e.g., lidar, radar.

Prof. Dr. Andrea Garzelli
Dr. Simone Lolli
Prof. Dr. Kai Qin
Prof. Dr. Yuanjian Yang
Guest Editors

Manuscript Submission Information

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Keywords

Multisensor data processing
Fusion of multisource remote sensing data
Classification and change detection from heterogeneous sensors
Multitemporal analysis

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Published Papers (8 papers)

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22 pages, 41841 KiB

Open AccessArticle

Advantages of Nonlinear Intensity Components for Contrast-Based Multispectral Pansharpening

by Alberto Arienzo, Luciano Alparone, Andrea Garzelli and Simone Lolli

Remote Sens. 2022, 14(14), 3301; https://doi.org/10.3390/rs14143301 - 8 Jul 2022

Cited by 8 | Viewed by 1671

Abstract

In this study, we investigate whether a nonlinear intensity component can be beneficial for multispectral (MS) pansharpening based on component-substitution (CS). In classical CS methods, the intensity component is a linear combination of the spectral components and lies on a hyperplane in the vector space that contains the MS pixel values. Starting from the hyperspherical color space (HCS) fusion technique, we devise a novel method, in which the intensity component lies on a hyper-ellipsoidal surface instead of on a hyperspherical surface. The proposed method is insensitive to the format of the data, either floating-point spectral radiance values or fixed-point packed digital numbers (DNs), thanks to the use of a multivariate linear regression between the squares of the interpolated MS bands and the squared lowpass filtered Pan. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the intensity component no longer lie on a hypersphere in the vector space of the MS samples, but on a hyperellipsoid. Furthermore, before the fusion is accomplished, the interpolated MS bands are corrected for atmospheric haze, in order to build a multiplicative injection model with approximately de-hazed components. Experiments on GeoEye-1 and WorldView-3 images show consistent advantages over the baseline HCS and a performance slightly superior to those of some of the most advanced methods. Full article

(This article belongs to the Special Issue Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications)

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17 pages, 3524 KiB

Open AccessArticle

Precipitation Microphysics of Tropical Cyclones over Northeast China in 2020

by Aoqi Zhang, Yilun Chen, Xiao Pan, Yuanyuan Hu, Shumin Chen and Weibiao Li

Remote Sens. 2022, 14(9), 2188; https://doi.org/10.3390/rs14092188 - 3 May 2022

Cited by 13 | Viewed by 2531

Abstract

Landfalling tropical cyclones (TCs) in Northeast China are rare because of the region’s high latitude (>40°N). In 2020, Northeast China was affected by three TCs within half a month—the first time on record. We used the Global Precipitation Measurement orbital dataset to study the precipitation microphysics during the TC period in Northeast China in 2020 (2020-TC), and during September in this region from 2014 to 2019 (hereafter September 2014–September 2019). FY-4A was used to provide cloud top height (CTH). The results show that, compared with September 2014–September 2019, the 2020-TC precipitation has stronger precipitation ice productivity, weaker deposition efficiency, stronger riming, and stronger coalescence processes. The storm top height (STH), CTH, and the difference between the two (CTH-STH) are indicative of the near-surface droplet size distribution (DSD), but there are differences: STH and CTH-STH both correlate significantly with mean mass-weighted drop diameter, whereas only the positive correlation between CTH and normalized drop concentration parameter passes the significance test. These results reveal for the first time the precipitation microphysics of landfalling TCs in Northeast China, and allow discussion of the validity of convective intensity indicators from the perspective of DSD. Full article

(This article belongs to the Special Issue Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications)

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20 pages, 4491 KiB

Open AccessArticle

Quantitatively Assessing the Contributions of Dust Aerosols to Direct Radiative Forcing Based on Remote Sensing and Numerical Simulation

by Jinyan Wang, Shixiang Su, Zelun Yin, Caixia Sun, Xiangshan Xie, Tianyu Wang, Dilinuer Yasheng, Jinche Chen, Xin Zhang and Yi Yang

Remote Sens. 2022, 14(3), 660; https://doi.org/10.3390/rs14030660 - 29 Jan 2022

Cited by 5 | Viewed by 3047

Abstract

Dust aerosols substantially impinge on the Earth’s climate by altering its energy balance, particularly over Northwest China, where dust storms occur frequently. However, the quantitative contributions of dust aerosols to direct radiative forcing (DRF) are not fully understood and warrant in-depth investigations. Taking a typical dust storm that happened during 9–12 April 2020 over Northwest China as an example, four simulation experiments based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) were designed, including a real scenario with dust emissions and three hypothetical scenarios without dust emissions, with dust emissions doubled, and with dust emissions reduced by half, to quantitatively evaluate the contributions of dust aerosols to DRF and then to surface temperature, with particular attention to the differences between daytime and nighttime. Moreover, multi-satellite observations were used to reveal the behavior of dust events and to evaluate the model performance. During the daytime, the net dust radiative forcing induced by dust aerosols was −3.76 W/m² at the surface (SFC), 3.00 W/m² in the atmosphere (ATM), and −0.76 W/m² at the top of the atmosphere (TOA), and thus led to surface air temperature cooling by an average of −0.023 °C over Northwest China. During the nighttime, the net dust radiative forcing was 2.20 W/m² at the SFC, −2.65 W/m² in the ATM, and −0.45 W/m² at the TOA, which then resulted in surface temperature warming by an average of 0.093 °C over Northwest China. These results highlight that the contribution of dust aerosols to DRF is greater during the daytime than that during the nighttime, while exhibiting the opposite impact on surface temperature, as dust can slow down the rate of surface temperature increases (decreases) by reducing (increasing) the surface energy during the daytime (nighttime). Our findings are critical to improving the understanding of the climate effects related to dust aerosols and provide scientific insights for coping with the corresponding disasters induced by dust storms in Northwest China. Full article

(This article belongs to the Special Issue Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications)

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21 pages, 20905 KiB

Open AccessArticle

Comparison of Macro- and Microphysical Properties in Precipitating and Non-Precipitating Clouds over Central-Eastern China during Warm Season

by Xiaoyi Zheng, Yuanjian Yang, Ye Yuan, Yanan Cao and Jinlan Gao

Remote Sens. 2022, 14(1), 152; https://doi.org/10.3390/rs14010152 - 30 Dec 2021

Cited by 4 | Viewed by 2951

Abstract

The macro- and microphysical properties of clouds can reflect their vertical physical structure and evolution and are important indications of the formation and development of precipitation. We used four-year merged CloudSat-CALIPSO-MODIS products to distinguish the macro- and microphysical properties of precipitating and non-precipitating clouds over central-eastern China during the warm season (May–September). Our results showed that the clouds were dominated by single- and double-layer forms with occurrence frequencies > 85%. Clouds with a low probability of precipitation (POP) were usually geometrically thin. The POP showed an increasing trend with increases in the cloud optical depth, liquid water path, and ice water path, reaching maxima of 50%, 60%, and 75%, respectively. However, as cloud effective radius (CER) increased, the POP changed from an increasing to a decreasing trend for a CER > 22 μm, in contrast with our perception that large particles fall more easily against updrafts, but this shift can be attributed to the transition of the cloud phase from mixed clouds to ice clouds. A high POP > 60% usually occurred in mixed clouds with vigorous ice-phase processes. There were clear differences in the microphysical properties of non-precipitating and precipitating clouds. In contrast with the vertical evolution of non-precipitating clouds with weaker reflectivity, precipitating clouds were present above 0 dBZ with a significant downward increase in reflectivity, suggesting inherent differences in cloud dynamical and microphysical processes. Our findings highlight the differences in the POP of warm and mixed clouds, suggesting that the low frequency of precipitation from water clouds should be the focus of future studies. Full article

(This article belongs to the Special Issue Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications)

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22 pages, 6418 KiB

Open AccessArticle

Assimilating C-Band Radar Data for High-Resolution Simulations of Precipitation: Case Studies over Western Sumatra

by Bojun Zhu, Zhaoxia Pu, Agie Wandala Putra and Zhiqiu Gao

Remote Sens. 2022, 14(1), 42; https://doi.org/10.3390/rs14010042 - 23 Dec 2021

Cited by 5 | Viewed by 3020

Abstract

Accurate high-resolution precipitation forecasts are critical yet challenging for weather prediction under complex topography or severe synoptic forcing. Data fusion and assimilation aimed at improving model forecasts, as one possible approach, has gained increasing attention in past decades. This study investigates the influence of the observations from a C-band Doppler radar over the west coast of Sumatra on high-resolution numerical simulations of precipitation around its vicinity under the Madden–Julian oscillation (MJO) in January and February 2018. Cases during various MJO phases were selected for simulations with an advanced research version of the weather research and forecasting (WRF) model at a cloud-permitting scale (~3 km). A 3-dimensional variational (3DVAR) data assimilation method and a hybrid three-dimensional ensemble–variational data assimilation (3DEnVAR) method, based on the NCEP Gridpoint Statistical Interpolation (GSI) assimilation system, were used to assimilate the radar reflectivity and the radial velocity data. The WRF-simulated precipitation was validated with the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data, and the fractions skill score (FSS) was calculated in order to evaluate the radar data impacts objectively. The results show improvements in the simulated precipitation with hourly radar data assimilation 6 h prior to the simulations. The modifications with assimilation were validated through the observation departure and moist convection. It was found that forecast improvements are relatively significant when precipitation is more related to local-scale convection but rather small when the background westerly wind is strong under the MJO active phase. The additional simulation experiments, under a 1- or 2-day assimilation cycle, indicate better improvements in the precipitation simulation with 3DEnVAR radar assimilation than those with the 3DVAR method. Full article

(This article belongs to the Special Issue Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications)

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21 pages, 9212 KiB

Open AccessArticle

Reproducibility of Pansharpening Methods and Quality Indexes versus Data Formats

by Alberto Arienzo, Bruno Aiazzi, Luciano Alparone and Andrea Garzelli

Remote Sens. 2021, 13(21), 4399; https://doi.org/10.3390/rs13214399 - 31 Oct 2021

Cited by 12 | Viewed by 2399

Abstract

In this work, we investigate whether the performance of pansharpening methods depends on their input data format; in the case of spectral radiance, either in its original floating-point format or in an integer-packed fixed-point format. It is theoretically proven and experimentally demonstrated that methods based on multiresolution analysis are unaffected by the data format. Conversely, the format is crucial for methods based on component substitution, unless the intensity component is calculated by means of a multivariate linear regression between the upsampled bands and the lowpass-filtered Pan. Another concern related to data formats is whether quality measurements, carried out by means of normalized indexes depend on the format of the data on which they are calculated. We will focus on some of the most widely used with-reference indexes to provide a novel insight into their behaviors. Both theoretical analyses and computer simulations, carried out on GeoEye-1 and WorldView-2 datasets with the products of nine pansharpening methods, show that their performance does not depend on the data format for purely radiometric indexes, while it significantly depends on the data format, either floating-point or fixed-point, for a purely spectral index, like the spectral angle mapper. The dependence on the data format is weak for indexes that balance the spectral and radiometric similarity, like the family of indexes, Q2ⁿ, based on hypercomplex algebra. Full article

(This article belongs to the Special Issue Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications)

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18 pages, 4518 KiB

Open AccessArticle

Detection of a Dust Storm in 2020 by a Multi-Observation Platform over the Northwest China

by Lili Yang, Zhiyuan Hu, Zhongwei Huang, Lina Wang, Wenyu Han, Yanping Yang, Huijie Tao and Jing Wang

Remote Sens. 2021, 13(6), 1056; https://doi.org/10.3390/rs13061056 - 10 Mar 2021

Cited by 20 | Viewed by 3098

Abstract

Dust storms have occurred frequently in northwest China and can dramatically reduce visibility and exacerbate air quality in downwind regions through long-range transport. In order to study the distribution characteristics of dust particles sizes, structures and concentrations in the process of dust storm, especially for the vertical distributions, the multi-observation platform composed of six Lidars and nine aerosol analytical instruments is first used to detect a severe dust storm event, which occurred in Northwest China on 3 May 2020. As a strong weather system process, the dust storm has achieved high intensity and wide range. When the intensity of a dust storm is at its strongest, the ratios of PM_2.5 (particulate matter with diameter < 2.5 µm) and PM₁₀ (particulate matter with diameter < 10 µm) (PM_2.5/PM₁₀) in cities examined were less than 0.2 and the extinction coefficients became greater than 1 km⁻¹ based on Lidar observations. In addition, the growth rates of PM_2.5 were higher than that of PM₁₀. The dust particles mainly concentrated at heights of 2 km, after being transported about 200–300 km, vertical height increased by 1–2 km. Meanwhile, the dust concentration decreased markedly. Furthermore, the depolarization ratio showed that dust in the Tengger Desert was dominated by spherical particles. The linear relationships between 532 nm extinction coefficient and the concentration of PM_2.5 and PM₁₀ were found firstly and their R² were 0.706 to 0.987. Our results could give more information for the physical schemes to simulate dust storms in specific models, which could improve the forecast of dust storms. Full article

(This article belongs to the Special Issue Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications)

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17 pages, 20487 KiB

Open AccessTechnical Note

Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

by Yuanzhi Cai, Hong Huang, Kaiyang Wang, Cheng Zhang, Lei Fan and Fangyu Guo

Remote Sens. 2021, 13(7), 1367; https://doi.org/10.3390/rs13071367 - 2 Apr 2021

Cited by 20 | Viewed by 4072

Abstract

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance. Full article

(This article belongs to the Special Issue Multisensor Data Fusion for Remote Sensing and Photogrammetry Applications)

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