Image registration is a critical process for the various applications in the remote sensing community, and its accuracy greatly affects the results of the subsequent applications. Image registration ...based on phase correlation has been widely concerned due to its robustness to gray differences and efficiency. After calculating the normalized cross-relation matrix Q, the most commonly used approach is fitting the 2-D phase plane that passes through the origin, but it needs to remove contaminated spectrum carefully and the corresponding parameters are empirical. In fact, the phase correlation matrix is rank one for a noise-free translation model. This property simplifies the matching problem to finding the best rank-one approximation of the normalized cross-relation matrix. We develop a novel algorithm that performs the rank-one matrix factorization on the phase correlation matrix by assuming its noise as mixture of Gaussian (MoG) distributions. The MoG model is a general approximator for any continuous distribution, and hence is able to model a wide range of noise distribution. The parameters of the MoG model can be evaluated under the framework of maximum likelihood estimation by using an expectation-maximization method, and the subspace is calculated with standard methods. The advantages of the algorithm, high accuracy, and robustness to aliasing, noise, gray difference, and occlusions are illustrated by a series of simulated and real-image experiments.
The successful launch of Luojia 1-01 complements the existing nighttime light data with a high spatial resolution of 130 m. This paper is the first study to assess the potential of using Luojia 1-01 ...nighttime light imagery for investigating artificial light pollution. Eight Luojia 1-01 images were selected to conduct geometric correction. Then, the ability of Luojia 1-01 to detect artificial light pollution was assessed from three aspects, including the comparison between Luojia 1-01 and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the source of artificial light pollution and the patterns of urban light pollution. Moreover, the advantages and limitations of Luojia 1-01 were discussed. The results showed the following: (1) Luojia 1-01 can detect a higher dynamic range and capture the finer spatial details of artificial nighttime light. (2) The averages of the artificial light brightness were different between various land use types. The brightness of the artificial light pollution of airports, streets, and commercial services is high, while dark areas include farmland and rivers. (3) The light pollution patterns of four cities decreased away from the urban core and the total light pollution is highly related to the economic development. Our findings confirm that Luojia 1-01 can be effectively used to investigate artificial light pollution. Some limitations of Luojia 1-01, including its spectral range, radiometric calibration and the effects of clouds and moonlight, should be researched in future studies.
Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or ...dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.
The worldwide spread of the COVID-19 epidemic has led to a rise in the costs of natural resources, which has increased production prices, slowed productivity, and threatened financial development. To ...stimulate the growth of sustainable economies, fiscal and monetary strategies must adopt a prioritized approach towards fostering innovation and development. The study investigates into recovery strategies by examining the influence of minute taxation reductions on power and exploring the incentives and mechanisms that drive innovation. We can estimate and deduce several outcomes by employing a variance-variance method to analyze quarterly data from Chinese companies listed in the market between Q1 2019 and Q2 2021. Enhancing energy efficiency through tax incentives can immensely benefit a company's innovative endeavors, as innovation serves to recover and expand market share. Furthermore, our research suggests that tax credits promoting energy efficiency can alleviate financial barriers and foster increased investment in innovation. Lastly, by endorsing artistic ventures, businesses can reduce costs and bolster internal cash flow. The implications of these findings are insignificant, as they propose that ineffective eco-design fiscal policies may serve as a negligible component of a limited business transformation plan for the post-COVID-19 era.
Display omitted
Cloud detection is critical in remote sensing image processing, and convolutional neural networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily focus on ...extracting local features, which can be challenging for cloud detection due to the variability in the size, shape, and boundaries of clouds. To address this limitation, we propose a hybrid Swin transformer–CNN cloud detection (STCCD) network that combines the strengths of both architectures. The STCCD network employs a novel dual-stream encoder that integrates Swin transformer and CNN blocks. Swin transformers can capture global context features more effectively than traditional CNNs, while CNNs excel at extracting local features. The two streams are fused via a fusion coupling module (FCM) to produce a richer representation of the input image. To further enhance the network’s ability in extracting cloud features, we incorporate a feature fusion module based on the attention mechanism (FFMAM) and an aggregation multiscale feature module (AMSFM). The FFMAM selectively merges global and local features based on their importance, while the AMSFM aggregates feature maps from different spatial scales to obtain a more comprehensive representation of the cloud mask. We evaluated the STCCD network on three challenging cloud detection datasets (GF1-WHU, SPARCS, and AIR-CD), as well as the L8-Biome dataset to assess its generalization capability. The results show that the STCCD network outperformed other state-of-the-art methods on all datasets. Notably, the STCCD model, trained on only four bands (visible and near-infrared) of the GF1-WHU dataset, outperformed the official Landsat-8 Fmask algorithm in the L8-Biome dataset, which uses additional bands (shortwave infrared, cirrus, and thermal).
The Yemen conflict has caused a severe humanitarian crisis. This study aims to evaluate the Yemen crisis by making use of time series nighttime light images from the Suomi National Polar-Orbiting ...Partnership Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS). We develop a process flow to correct NPP-VIIRS nighttime light from April 2012 to March 2017 by employing the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light image. The time series analyses at national scales show that there is a sharp decline in the study period from February 2015 to June 2015 and that the total nighttime light (TNL) of Yemen decreased by 71.60% in response to the decline period. The nighttime light in all provinces also showed the same decline period, which indicates that the Saudi-led airstrikes caused widespread and severe humanitarian crisis in Yemen. Spatial pattern analysis shows that the areas of declining nighttime light are mainly concentrated in Sana’a, Dhamar, Ibb, Ta’izz, ’Adan, Shabwah and Hadramawt. According to the validation with high-resolution images, the decline in nighttime light in Western cities is caused by the damage of urban infrastructure, including airports and construction; moreover, the reason for the decline in nighttime light in eastern cities is the decrease in oil exploration. Using nighttime light remote sensing imagery, our findings suggest that war made Yemen dark and provide support for international humanitarian assistance organizations.
Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation ...model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is simple, because it contains only two parts: one is the backbone of the network, which is used to learn the local features (short distance context information around the pixel) of buildings from the image; the other part is the pyramid self-attention module, which is used to obtain the global features (long distance context information with other pixels in the image) and the comprehensive features (includes color, texture, geometric and high-level semantic feature) of the building. The network is an end-to-end approach. In the training stage, the input is the remote sensing image and corresponding label, and the output is probability map (the probability that each pixel is or is not building). In the prediction stage, the input is the remote sensing image, and the output is the extraction result of the building. The complexity of the network structure was reduced so that it is easy to implement. The proposed PISANet was tested on two datasets. The result shows that the overall accuracy reached 94.50 and 96.15%, the intersection-over-union reached 77.45 and 87.97%, and F1 index reached 87.27 and 93.55%, respectively. In experiments on different datasets, PISANet obtained high overall accuracy, low error rate and improved integrity of individual buildings.
As common contaminants, metals are non-negligible risk factors for diabetes and chronic kidney disease. However, whether there is an association between multiple metals exposure and incident chronic ...kidney disease (CKD) risk in patients with diabetes is unclear. We conducted a prospective study to evaluate these associations. In total, 3071 diabetics with baseline estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m2 from the Dongfeng-Tongji cohort were included. We measured baseline plasma concentrations of 23 metals and investigated the associations between plasma metal concentrations and CKD in diabetics using logistic regression, the least absolute shrinkage and selection operator (LASSO), and the Bayesian Kernel Machine Regression (BKMR) models. During average 4.6 years of follow-up, 457 diabetics developed CKD (14.9 %). The three models consistently found plasma levels of zinc, arsenic, and rubidium had a positive association with incident CKD risk in patients with diabetes, while titanium, cadmium, and lead had an inverse correlation. The results of BKMR showed a significant and positive overall effect of 23 metals on the risk of CKD, when all of the metals were above the 50th percentile as compared to the median value. In addition, potential interactions of zinc and arsenic, zinc and cadmium, zinc and lead, titanium and arsenic, and cadmium and lead on CKD risk were observed. In summary, we found significant associations of plasma titanium, zinc, arsenic, rubidium, cadmium, and lead with CKD in diabetes and interactions between these metals except for rubidium. Co-exposure to multiple metals was associated with increased CKD risk in diabetics.
•The logistic, LASSO, and BKMR models were used to examine the associations.•Plasma Ti, Cd, and Pb were negatively associated with CKD in diabetes.•Plasma Zn, As, and Rb were positively associated with CKD in diabetes.•Ti, Zn, As, Cd, and Pb had significant interaction effects on CKD risk in diabetes.
As a vital land cover type, impervious surface directly reflects human activities and urbanization, significantly impacting the environment, climate, and biodiversity, especially in ecologically ...fragile areas such as the Qinghai–Tibet Plateau (QTP) in China. Thus, precise knowledge of impervious surface information on the QTP is essential for its ecological protection and social development. In order to improve the application of products and inform further studies, we assessed the accuracy of seven medium resolution (10–30 m) impervious surface products in the QTP, including GAIA, CISC, GlobalLand30 (GL30), GLC-FCS30 (FCS30), GHS-BUILT-S2 (GHSB), ESA WorldCover10 (WC10), and Dynamic World NRT products (DW). The validation set labeled according to domestic GF-1 images was used to calculate the precision, recall, and F1-Score of these products, and two impervious surface vote maps were generated to analyze their spatial consistency. The results showed that CISC and DW had the highest overall quality among the 30 m and 10 m products, with F1-Scores of 0.5701 and 0.5670, respectively. We also validated the accuracy of different data combinations and their intersection and union sets to provide guidance based on the results for data selection in impervious surface studies on the QTP. For results calculated by the strict validation set, which was exclusive of mixed grids, precision decreased slightly while recall increased significantly for all products, indicating that the omissions were mostly mixed pixels with a smaller percentage of impervious surface. In terms of spatial consistency, the maximum impervious surface range voted by the seven products jointly only accounts for 0.82% of the QTP, which is 2,786,800 km2 in total. Additionally, the high consistency area (votes > 4), with a distribution concentrated in large cities and dense buildings, only accounts for 15.18% of this maximum range. In summary, each product’s regional accuracy in the QTP was lower than their published accuracy, and they omitted many impervious surfaces, especially those with a background of bare land.
High-resolution thermal infrared (TIR) remote sensing images can more accurately retrieve land surface temperature and describe the spatial pattern of urban thermal environment. The Thermal Infrared ...Spectrometer (TIS), which has high spatial resolution among spaceborne thermal infrared sensors at present, and global data acquisition capability, is one of the sensors equipped in the SDGSAT-1. It is an important complement to the existing international mainstream satellites. In order to produce standard data products, rapidly and accurately, the automatic registration and geometric correction method needs to be developed. Unlike visible–visible image registration, thermal infrared images are blurred in edge details and have obvious non-linear radiometric differences from visible images, which make it challenging for the TIR-visible image registration task. To address these problems, homomorphic filtering is employed to enhance TIR image details and the modified RIFT algorithm is proposed to achieve TIR-visible image registration. Different from using MIM for feature description in RIFT, the proposed modified RIFT uses the novel binary pattern string to descriptor construction. With sufficient and uniformly distributed ground control points, the two-step orthorectification framework, from SDGSAT-1 TIS L1A image to L4 orthoimage, are proposed in this study. The first experiment, with six TIR-visible image pairs, captured in different landforms, is performed to verify the registration performance, and the result indicates that the homomorphic filtering and modified RIFT greatly increase the number of corresponding points. The second experiment, with one scene of an SDGSAT-1 TIS image, is executed to test the proposed orthorectification framework. Subsequently, 52 GCPs are selected manually to evaluate the orthorectification accuracy. The result indicates that the proposed orthorectification framework is helpful to improve the geometric accuracy and guarantee for the subsequent thermal infrared applications.