Significant Scale Differences Research Articles

MFNet: Multi-scale feature enhancement networks for wheat head detection and counting in complex scene

Developing a high-precision wheat head detection algorithm is challenging due to the dense distribution and diverse sizes of wheat head in the field, as well as serious coverage from weeds. In this work, we propose multi-scale feature enhancement networks for wheat head detection and counting in complex scene (MFNet). Firstly, we introduce a deformable spatial attention mechanism (DSAM) and embed it in the backbone network to enhance the extraction of wheat head features while suppressing irrelevant features, effectively improving detection of wheat head in occluded environments. Secondly, we design a multi-scale receptive field feature fusion (MRFF) module in combination with an improved light-weight feature pyramid module to achieve more accurate detection of wheat head of different sizes and improve localization accuracy simultaneously. Additionally, the modified detection head with deformable convolution is able to adapt to different shapes of wheat head features and accurately predict the bounding boxes. Our method achieves 94.2% AP@50 at a speed of 30 FPS on the GWHD dataset, and to verify the generalization of the proposed method, we constructed a dense wheat head detection (DWHD) dataset with annotations, conducted experiments on the DWHD and SPIKE datasets and compared them with state-of-the-art algorithms. The experimental results show that the proposed method outperformed most of the existing methods, which further proved the superiority and robustness of the proposed method, and demonstrated that our model possesses excellent adaptability, enabling it to flexibly cope with scenarios of significant scale differences and severe occlusion in complex field environments for wheat head, and provide a technological reference for monitoring the wheat phenotype.

The Effect of Domain Length and Initialization Noise on Direct Numerical Simulation of Shear Stratified Turbulence

Direct numerical simulation (DNS) has been employed with success in a variety of oceanographic applications, particularly for investigating the internal dynamics of Kelvin–Helmholtz (KH) billows. However, it is difficult to relate these results directly with observations of ocean turbulence due to the significant scale differences involved (ocean shear layers are typically on the order of tens to hundreds of meters in thickness, compared to DNS studies, with layers on the order of one to tens of centimeters). As efforts continue to inform our understanding of geophysical-scale turbulence by extrapolating DNS results, it is important to understand the impact of model setup and initial conditions on the resulting turbulent quantities. Given that geophysical-scale measurements, whether through microstructures or other techniques, can only provide estimates of averaged TKE quantities (e.g., TKE dissipation or buoyancy flux), it may be necessary to compare mean turbulent quantities derived from DNS (i.e., across one or more complete billow evolutions) with ocean measurements. In this study, we analyze the effect of domain length and initial velocity noise on resulting turbulent quantities. Domain length is important, as dimensions that are not integer multiples of the natural KH billow wavelength may compress or stretch the billows and impact their energetics. The addition of random noise in the initial velocity field is often used to trigger turbulence and suppress secondary instabilities; however, the impact of noise on the resulting turbulent energetics is largely unknown. In this study, we conclude that domain lengths on the order of 1.5 times the natural wavelength or less can affect the resulting turbulent energetics by a factor of two or more. We also conclude that increasing the amplitude of random initial velocity noise decreases the resulting turbulent energetics, but that different realizations of the random noise field may have an even greater impact than amplitude. These results should be considered when designing a DNS experiment.

An object detection method for catenary component images based on improved Faster R-CNN

Catenary components are an important part of electrified railways. Especially for catenary support devices, there are various types of components with significant differences in scale. According to statistical data, there is a high risk of failure for the catenary support device components during the operation of the catenary system. Therefore, in order to ensure the safe operation of the railways, it is critical to accurately locate and recognize the components in the catenary images. In this paper, we propose an improved method based on faster region-based convolutional neural networks (Faster R-CNN) framework to realize the detection and extraction of the components on the catenary support devices. Firstly, the anchor box parameters are reset using the K-means clustering method, which greatly improves the localization precision of the predicted box. Secondly, scaled exponential linear units activation function is introduced to improve the algorithm performance. Moreover, ResNet-34, the backbone of Faster R-CNN, is optimized. We design a transition structure for multi-scale filter combination convolution to avoid missing feature information and eliminate some redundant convolution structures. This modification substantially enhances the capability of the model to recognize a wide variety of component types. Finally, we conduct some control experiments comparing with single shot multibox detector and you only look once (YOLO) series (YOLOv3, YOLOv5 and YOLOv7) models. They are faster but less accurate, especially for small objects. The results show that the proposed method has better detection performance, achieving a mean average precision of 96.50% and running at 17.79 frames per second. In addition, our model has the highest average recall of 69.27%, which is 2.66% higher than the original model.

Spatial–temporal evolution and land use transition of rural settlements in mountainous counties

BackgroundRural settlements are undergoing significant changes under the rapid urbanisation, and understanding their evolution characteristics and surrounding land use will provide a basis for land spatial planning. This study takes Pingnan County, Fujian Province, China as study area, reveals the characteristics of spatial–temporal evolution and surrounding land use transition of settlements during 1985–2020 through landscape metrics, spatial “hot spot” analysis, scale classification statistics, rank-size model, Gini index, land use transition matrix.ResultsThe results show that: (1) Concerning the size and morphological characteristics, the settlements have witnessed a considerable increase in number and scale while remaining stable in shape. (2) Regarding spatial distribution characteristics, the settlements became more evenly spread, forming three main hotspot clusters. (3) Concerning scale structure characteristics, there are significant differences in scale, growth rates, and polarisation of settlements; the polarisation of large settlements shifted from a marked divergence before 2010 to a more balanced trend after 2010. (4) The land use transition around settlements differed in buffer zones and periods. During 1985–2010, settlement expansion heavily depended on cropland, depleting nearby resources, with an increase of woodland and grassland. During 2010–2020, expansion integrated cropland, woodland, and grassland, with cropland growth mainly encroaching on woodland and grassland.ConclusionsThe study's findings are significant for optimising rural settlement structure in mountains and promoting sustainable land resource use.

Translating pumping test data into groundwater model parameters: a workflow to reveal aquifer heterogeneities and implications in regional model parameterization

Groundwater modelers frequently grapple with the challenge of integrating aquifer test interpretations into parameters used by regional models. This task is complicated by issues of upscaling, data assimilation, and the need to assign prior probability distributions to numerical model parameters in order to support model predictive uncertainty analysis. To address this, we introduce a new framework that bridges the significant scale differences between aquifer tests and regional models. This framework also accounts for loss of original datasets and the heterogeneous nature of geological media in which aquifer testing often takes place. Using a fine numerical grid, the aquifer test is reproduced in a way that allows stochastic representation of site hydraulic properties at an arbitrary level of complexity. Data space inversion is then used to endow regional model cells with upscaled, aquifer-test-constrained realizations of numerical model properties. An example application demonstrates that assimilation of historical pumping test interpretations in this manner can be done relatively quickly. Furthermore, the assimilation process has the potential to significantly influence the posterior means of decision-pertinent model predictions. However, for the examples that we discuss, posterior predictive uncertainties do not undergo significant reduction. These results highlight the need for further research.

Multi-scale fusion for few-shot remote sensing image classification

ABSTRACT Few-shot image classification is currently valuable research in the field of remote sensing image applications. The difficulty in obtaining training data for a large number of labelled remote sensing images leads to difficulties in applying traditional deep learning-based remote sensing image classification methods. Existing few-shot remote sensing image classification methods usually use a large number of base datasets to train network models in the pre-training phase and perform few-shot classification tasks after model fine-tuning on new datasets in the meta-testing phase. However, an insufficient number of remote sensing images lead to inaccurate features being extracted by the model, and the large amount of new class data for fine-tuning is also not easily accessible. In addition, using a pre-trained model to extract features from the new dataset will create a”negative transfer” problem. In this paper, we aim to address the above challenges in two ways. First, we use a subset of ImageNet, a readily available few-shot natural image dataset, for model pre-training, and eliminate the fine-tuning operation to simulate a real-world application scenario where remote sensing data is extremely scarce. Second, due to the significant differences in scale and style between the ImageNet dataset and the remote sensing dataset, which lead to a serious negative transfer problem in the model, we design a multi-scale feature fusion module to comprehensively decide the labels of query samples considering all scales to compensate for the scale differences and alleviate the”negative transfer” problem. We conducted experiments on four benchmark remote sensing datasets and achieved satisfactory performance.

Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images.

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

FloU-Net: An Optical Flow Network for Multimodal Self-Supervised Image Registration

Image registration is an essential task in image processing, where the final objective is to geometrically align two or more images. In remote sensing, this process allows comparing, fusing or analyzing data, specially when multi-modal images are used. In addition, multi-modal image registration becomes fairly challenging when the images have a significant difference in scale and resolution, together with local small image deformations. For this purpose, this paper presents a novel optical flow-based image registration network, named the FloU-Net, which tries to further exploit inter-sensor synergies by means of deep learning. The proposed method is able to extract spatial information from resolution differences and through an U-Net backbone generate an optical flow field estimation to accurately register small local deformations of multi-modal images in a self-supervised fashion. For instance, the registration between Sentinel-2 (S2) and Sentinel-3 (S3) optical data is not trivial, as there are considerable spectral-spatial differences among their sensors. In this case, the higher spatial resolution of S2 result in S2 data being a convenient reference to spatially improve S3 products, as well as those of the forthcoming Fluorescence Explorer (FLEX) mission, since image registration is the initial requirement to obtain higher data processing level products. To validate our method, we compare the proposed FloU-Net with other state-of-the-art techniques using 21 coupled S2/S3 optical images from different locations of interest across Europe. The comparison is performed through different performance measures. Results show that proposed FloU-Net can outperform the compared methods. The code and dataset are available in https://github.com/ibanezfd/FloU-Net.

Self-Attention Guidance and Multiscale Feature Fusion-Based UAV Image Object Detection

Object detection on UAV images is a recent research hotspot. Existing object detection methods have achieved good results on general scenes, but there are inherent challenges with UAV images. The detection accuracy of UAV images is limited by complex backgrounds, significant scale differences, and densely arranged small objects. To solve these problems, we propose a UAV image object detection network based on Self-attention Guidance and Multi-scale Feature fusion (SGMFNet). Firstly, we design a Global-Local Feature Guidance module (GLFG). This module can effectively combine local information and global information, which makes the model focus on the object area and reduces the impact of complex background. Secondly, an improved Parallel Sampling Feature Fusion module (PSFF) is designed to efficiently fuse multi-scale features. Thirdly, we design an Inverse-residual Feature Enhancement module (IFE), which is embedded in the front of the newly added detection head to enhance feature extraction on small objects. Finally, we conduct a large number of experiments on the VisDrone2019 dataset. The results show that the proposed SGMFNet outperforms other popular methods, and has achieved good results in many scenarios.

A coupled combustion and hydrodynamic model for the prediction of waterwall tube overheating of supercritical boiler

Tube overheating is the leading cause of boiler tube failures that had led to tremendous replacement and maintenance costs. Boiler tube temperature is simultaneously affected by the heating effect of hot combustion gas in the furnace and the cooling effect of steam flow in boiler tubes. Thus, prediction of tube temperature needs to include the simulations of both the gas and steam flows of the boiler in the model. To compromise the significant scale difference between the boiler furnace and tubes, a coupled model that integrates a three-dimensional (3D) CFD model describing the gas flow and combustion processes in the furnace with a one-dimensional (1D) hydrodynamic model describing the steam flow in the boiler waterwall tubes was developed in this paper. The 3D CFD and 1D hydrodynamic models are coupled by iterative data exchange of boiler heat flux and steam temperature between the two models. In this way, this coupled model can incorporate all the key parameters that strongly affect the waterwall tube temperature, including the distributions of boiler heat flux, steam temperature and the convective heat transfer coefficient of steam flow in boiler tubes. In particular, the critical effects of steam maldistribution caused by both the structure of tube network system and uneven heat absorption of parallel tubes on tube temperature can be resolved by this model. This coupled model provides the capability to predict the detailed distribution of waterwall tube temperature and locate the areas of tube overheating under different boiler operating parameters. This coupled model was employed to predict the tube temperature distribution of the spiral waterwall of a 350 MW supercritical boiler. The predicted results show that the areas having higher risk of tube overheating are located near the outlet of spiral waterwall tubes where the steam temperature is the highest and on the wall areas where the local wall heat flux is the highest. The results strengthen the necessity of incorporating both the gas and steam sides of boiler into the model for accurate prediction of waterwall tube temperature.

Electroplasticity mechanism study based on dislocation behavior of Al6061 in tensile process

The thermal and athermal influence of electric current on dislocation motion is the fundamental mechanism for the electroplastic effect in different metals. Due to the significant scale difference between dislocation evolution and macroscopic deformation, it is difficult to figure out how the electric current affects the dislocation motion and further leads to the macroscopic electroplastic behavior since several interweaving mobile and immobile dislocations are involved. As a macroscopic manifestation of the interaction between dislocations and interstitial atoms, the Portevin–Le Chatelier (PLC) effect provides a satisfying entry point for solving the above problems by connecting the microscopic dislocation mechanism to the macro-scale electroplastic effect in a straightforward manner. Experiments of Al6061 in the tensile process show that the introduction of current causes two opposite effects, i.e., an increase of the pinning strength and a reduction of the time for dislocations to escape from pinning. Further microscopic characterization reveals that the reasons for the above phenomena include two aspects: (1) Promotion of the dissolution of the precipitated phase and increases in the concentration of interstitial atoms in the system. (2) Promotion of the motion of dislocations and the activation of additional slip systems. Those changes are closely related to the thermal and athermal effects of the current. Based on the observation results in this paper, the contradictions in the existing literature can be well explained. • Study on the influence of electric current on dislocation behavior based on the Portevin-le Chatelier effect. • The influence of currents on dislocation motion has two competing mechanisms related to thermal and athermal effects. • The research results successfully explained the contradictions in the existing research.

A Dynamic Pore Network Model for Imbibition Simulation Considering Corner Film Flow

AbstractWetting films can develop in the corners of angular pores under strong wetting conditions. Modeling the dynamics of corner film remains elusive using direct numerical simulations because of the significant scale difference between main meniscus and corner film flow. In this paper, the modified interacting capillary bundle model (ICB), developed in our previous work to describe accurately corner film dynamics in a single square tube, is incorporated into a single‐pressure dynamic pore network model (DPNM) to simulate imbibition in strongly wetting porous media with corner film flow. The traditional pore network is decomposed into several layers of interacting subpore networks where the 0th layer of subpore network simulates the main meniscus flow and higher layers the corner film flow. The fluid flow between different layers is captured by interlayer throats. In addition, the snap‐off mechanism caused by the thickening of wetting corner film is considered. The accuracy of the developed model is validated for four cases: spontaneous imbibition in a single square tube, wetting fluid redistribution through corner films under a capillary pressure difference, snap off in a narrow throat connecting two large pores, and imbibition dynamics in a real microfluidic porous geometry. The validated model is then used to simulate both spontaneous and controlled imbibition in a pore network with random pore size distribution. The interaction between corner film and main meniscus flow in porous media is analyzed from a pore‐scale perspective.

Relationship Between Emotional Intelligence And Attitude Towards Peer’s Success And Failure In Adolescence And Youth

IntroductionThe problems of the attitude to success and failure are relevant at the present time.Objectivesstudy allows to describe and compare the indicators of emotional intelligence and the types of attitudes toward success and failure of a peer in adolescence and youthMethods110 students and 90 teenagers were studied. T.V. Beskova’s methods “Attitude to the success and/or failure of a peer” in adolescence and adolescence (adapted technique by T.V. Beskova), N. Hall’s method of evaluating “emotional intelligence” (EQ questionnaire).ResultsThe “Relationship to Peer Success and Failure” methodology showed differences between adolescence and adolescence in scales: “peer joy” U = 0.016 (p≤0,05), “desire to achieve the same” U = 0.008 (p≤0,01), U = 0.027 “envy” (p≤0,05) and “passive” U = 0.006 (p≤0,01). The EQ questionnaire showed statistically significant differences in scales as “managing one’s emotions” U = 0.007 (p≤0,01), “self-motivation” U = 0.006 (p≤0,01). There are age differences in the types of attitudes to peer success and failure and the specifics of emotional intelligence in older adolescence and adolescence. Spearman rank correlation coefficient (r = 0.48) there is a relationship between the type of relationship to peer success and failure and emotional intelligence in adolescence and adolescenceConclusionsThus, the study showed the features of personal factors in relation to the success and failure of a peer in adolescence and adolescence.DisclosureNo significant relationships.

A graph-matching approach for cross-view registration of over-view and street-view based point clouds

Wide-area 3D data generation for complex urban environments often needs to leverage a mixed use of data collected from both air and ground platforms, such as from aerial surveys, satellite, and mobile vehicles. On one hand, such kind of data with information from drastically different views (ca. 90° and more) forming cross-view data, which due to very limited overlapping region caused by the drastically different line of sight of the sensors, is difficult to be registered without significant manual efforts. On the other hand, the registration of such data often suffers from non-rigid distortion of the street-view data (e.g., non-rigid trajectory drift), which cannot be simply rectified by a similarity transformation. In this paper, based on the assumption that the object boundaries (e.g., buildings) from the over-view data should coincide with footprints of façade 3D points generated from street-view photogrammetric images, we aim to address this problem by proposing a fully automated geo-registration method for cross-view data, which utilizes semantically segmented object boundaries as view-invariant features under a global optimization framework through graph-matching: taking the over-view point clouds generated from stereo/multi-stereo satellite images and the street-view point clouds generated from monocular video images as the inputs, the proposed method models segments of buildings as nodes of graphs, both detected from the satellite-based and street-view based point clouds, thus to form the registration as a graph-matching problem to allow non-rigid matches; to enable a robust solution and fully utilize the topological relations between these segments, we propose to address the graph-matching problem on its conjugate graph solved through a belief-propagation algorithm. The matched nodes will be subject to a further optimization to allow precise-registration, followed by a constrained bundle adjustment on the street-view image to keep 2D-3D consistencies, which yields well-registered street-view images and point clouds to the satellite point clouds. Our proposed method assumes no or little prior pose information (e.g. very sparse locations from consumer-grade GPS (global positioning system)) for the street-view data and has been applied to a large cross-view dataset with significant scale difference containing 0.5 m GSD (Ground Sampling Distance) satellite data and 0.005 m GSD street-view data, 1.5 km in length involving 12 GB of data. The experiment shows that the proposed method has achieved promising results (1.27 m accuracy in 3D), evaluated using collected LiDAR point clouds. Furthermore, we included additional experiments to demonstrate that this method can be generalized to process different types of over-view and street-view data sources, e.g., the open street view maps and the semantic labeling maps. Codes will be made available through Github Repository.1https://github.com/GDAOSU/graph-matching-based-crossview-registration.1

Feature Split–Merge–Enhancement Network for Remote Sensing Object Detection

Recently, multicategory object detection in high-resolution remote sensing images is still a challenge. First, objects with significant scale differences exist in one scene simultaneously, so it is generally difficult for the detectors to balance the detection performance of large and small objects. Second, because of the complex background and the objects&#x2019; densely distributed characteristics in the remote sensing images, the extracted features usually have noise and blurred boundaries, which interfere with the detection performance of the object detectors. With this observation, we propose an end-to-end scale-aware network called feature split&#x2013;merge&#x2013;enhancement network (SME-Net) for remote sensing object detection, composed of the feature split-and-merge (FSM) module, the offset-error rectification (OER) module, and the object saliency enhancement (OSE) strategy. FSM eliminates salient information of large objects to highlight the features of small objects in the shallow feature maps. It also transmits the effective detailed features of large objects to the deep feature maps, alleviating feature confusion between multiscale objects. OER corrects the inconsistency of the features spatial layout among the multilayer feature maps by the proposed offset loss, so as to achieve supervised elimination and transmission in FSM. OSE enhances the features of interests and suppresses the background information by the proposed membership function, thus preventing false detection and missed detection caused by noise and blurred boundaries. The effectiveness of the proposed algorithm has been verified on multiple datasets. Our code is available at: <uri>https://github.com/Momuli/SMENet.git</uri>

Large Diameter Piles Under Lateral Loading – A Database Study

Normal 0 false false false EN-US ZH-TW AR-SA /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Times New Roman,serif;} The question to be answered is: how well are current P-y curves predicting the behavior of large diameter piles subjected to monotonic lateral loading? Current P-y curves were developed about 60 years ago based on lateral load tests on piles which ranged from 0.3 to 0.6 m in diameter. Today’s pile diameters can reach 4 m or more. This significant difference in scale brings into question the applicability of these early P-y curves to today’s large diameter piles. A horizontal load tests database of 46 piles with diameters larger than 1.5 m (up to 3.0 m) and 64 piles with diameters less than 1.5 m both in sand and in clay was assembled. Predictions of load and displacement were carried out using commonly used P-y curves and the software LPILE. Within this paper, these predictions are compared to the measured loads and measured displacements. The ratio of predicted over measured quantities is plotted against pile diameter, and trends are noted. Modifications to the P-y curves are then proposed so that the ratio of predicted over measured displacement remains approximately independent of diameter. Finally, the probability that the predictions will be unsafe is evaluated.

Two Tales of the World: Comparison of Widely Used World News Datasets GDELT and EventRegistry

In this work, we compare GDELT and Event Registry, which monitor news articles worldwide and provide big data to researchers regarding scale, news sources, and news geography. We found significant differences in scale and news sources, but surprisingly, we observed high similarity in news geography between the two datasets. In this work, we compare GDELT and Event Registry, which monitor news articles worldwide and provide big data to researchers regarding scale, news sources, and news geography. We found significant differences in scale and news sources, but surprisingly, we observed high similarity in news geography between the two datasets.In this work, we compare GDELT and Event Registry, which monitor news articles worldwide and provide big data to researchers regarding scale, news sources, and news geography. We found significant differences in scale and news sources, but surprisingly, we observed high similarity in news geography between the two datasets.

Shadow education in the context of early tracking: between-track differences in the Czech Republic

ABSTRACT Previous research on the implications of early-tracking education systems has not specifically focused on how studying in academic and non-academic tracks shapes the features and characteristics of shadow education (private tutoring) that students are involved in. The study compares the scale and features of private tutoring and the underlying factors of its reception among lower-secondary students in the two tracks. Analysing a representative sample of 1,280 senior grade students, the study found significant differences in scale, subjects and reasons for tutoring during their lower-secondary studies, which may partly explain the prevailing gaps in between-track student achievements. Early tracking is likely to contribute to increasing the overall scale of PT by introducing selective entrance examinations, which nurture the demand for PT. While parental education, books at home, city size and educational aspirations were significant predictors of private tutoring for regular-track students, they were insignificant for academic-track students.

Highly thermally conductive and eco-friendly OH-h-BN/chitosan nanocomposites by constructing a honeycomb thermal network

More than 110,000,000 tons of mismanaged plastics were to be produced in 2020. Polymers are favored in the preparation of thermally conductive materials due to their excellent comprehensive properties. However, most polymers fabricated for thermally conductive materials are difficult to degrade in the natural environment. To alleviate the increasingly severe environmental problems, we reported a novel eco-friendly material with high thermal conductivity, which was composited of chitosan microspheres (CSM) and hydroxyl-functionalized hexagonal boron nitride (OH-h-BN) nanoplatelets. Utilizing their significant difference in scales, the OH-h-BN nanoplatelets were arranged between each CSM. Their overall structure was similar to the honeycomb: CSM were honeycomb cores, and OH-h-BN nanoplatelets were honeycomb network. The routine-structure OH-h-BN/CS nanocomposites were only 0.94 ± 0.02 W·m−1·K−1 at 50 wt% in thermal conductivity. However, the OH-h-BN/CSM nanocomposites with honeycomb structure can reach 5.66 ± 0.32 W·m−1·K−1 in the same loading, for enhancement of 502% and 1914% than OH-h-BN/CS nanocomposites and pure CS, respectively.

An Integrated Approach to Registration and Fusion of Hyperspectral and Multispectral Images

Combining a hyperspectral (HS) image and a multispectral (MS) image - an example of image fusion - can result in a spatially and spectrally high-resolution image. Despite the plethora of fusion algorithms in remote sensing, a necessary prerequisite, namely registration, is mostly ignored. This limits their application to well-registered images from the same source. In this article, we propose and validate an integrated registration and fusion approach (code available at https://github.com/zhouyuanzxcv/Hyperspectral). The registration algorithm minimizes a least-squares (LSQ) objective function with the point spread function (PSF) incorporated together with a nonrigid freeform transformation applied to the HS image and a rigid transformation applied to the MS image. It can handle images with significant scale differences and spatial distortion. The fusion algorithm takes the full high-resolution HS image as an unknown in the objective function. Assuming that the pixels lie on a low-dimensional manifold invariant to local linear transformations from spectral degradation, the fusion optimization problem leads to a closed-form solution. The method was validated on the Pavia University, Salton Sea, and the Mississippi Gulfport datasets. When the proposed registration algorithm is compared to its rigid variant and two mutual information-based methods, it has the best accuracy for both the nonrigid simulated dataset and the real dataset, with an average error less than 0.15 pixels for nonrigid distortion of maximum 1 HS pixel. When the fusion algorithm is compared with current state-of-the-art algorithms, it has the best performance on images with registration errors as well as on simulations that do not consider registration effects.