基于生成对抗网络的建筑物损毁检测EI北大核心CSCD

建筑物作为自然灾害中最受影响的承灾体之一,其损毁信息的准确提取对灾后应急救援具有十分重要的意义。本文借鉴多模态的思想,提出了一种自动检测损毁建筑物的recursive-generative adversarial networks(RS-GAN)方法,将损毁建筑物检测分为灾前建筑物识别和灾后损毁建筑物检测两个任务,且分别在两个GAN分支中完成。RS-GAN加入联合损失函数将两个GAN分支进行连接,充分利用两个任务之间的潜在互利性提升检测效果。RS-GAN利用第1条GAN分支识别建筑物灾前形状与位置,并将识别结果作为第2条GAN分支的输入进行损毁建筑物检测任务,从而使检测结果具有更清晰的轮廓。该方法为端到端模型,在不需要过多的人工干预情形下,实现了损毁建筑物的自动检测。为了验证RS-GAN模型的效果,在圣罗莎和密苏里两个数据集上进行了测试。试验结果表明,RS-GAN方法拥有更好的检测性能,在圣罗莎数据集上的总体精度和平均精度分别达到了0.90和0.86。     

Detail focused fusion of hyperspectral and multispectral imagesEI北大核心CSCD

Hyperspectral image (HSI) and multispectral image (MSI) are two types of images widely used in the field of remote sensing. These images are useful in certain applications, such as environmental monitoring, target detection, and mineral exploration. HSI contains a large amount of spectral information. Photons are typically collected in a larger spatial area on the sensor to ensure a sufficiently high signal-to-noise ratio (SNR). Accordingly, the HSI spatial resolution is much lower compared with MSI. This low spatial resolution greatly affects the practicality of HSI. Accordingly, fusing a low-spatial resolution HSI (LR-HSI) with a high-spatial resolution MSI (HR-MSI) in the same scene to obtain a high-resolution HSI (HR-HSI) is a method for solving such problems, which resolves the contradiction that the spatial resolution and the spectral resolution cannot simultaneously maintain a high level. From the analysis of fusion effect, the spatial and spectral reconstruction errors of the existing algorithms are mainly reflected in the edge and detail areas. The method proposed in this work was a fusion algorithm for dictionary construction and image reconstruction based on detail attention. In terms of maintaining spectral characteristics, the spectral distribution in the detail area is complex and diverse because of the proximity effect of the image. This work proposes to perform dictionary learning on the image and detail layers. The detail perception error terms and a constraint of edge adaptive directional total variation are proposed for spatial characteristic enhancement, which is combined with a local low rank constraint in the same fusion framework to estimate the sparse coefficient. Experiments were conducted on two datasets, namely, Pavia University and Indian Pine, to verify the effectiveness of the proposed method. The quantitative evaluation metrics contain peak SNR, relative dimensionless global error in synthesis, spectral angle map, and universal image quality index. Based on the experimental comparison, the fusion result of the algorithm proposed in this work is significantly improved compared with those of the other algorithms in terms of spatial and spectral characteristics. This work uses dictionary learning to propose a fusion algorithm for dictionary construction and image reconstruction with attention to details through the analysis of the existing hyperspectral and multispectral image fusion algorithms. A hierarchical dictionary learning algorithm is proposed to address the problem of large reconstruction error in the detail part of the existing algorithms. The detail perception error term and the direction adaptive full variational regularization term are used to improve the spectral dictionary solution and coefficient estimation, respectively. The result of the fusion is the error in the spectral characteristics and spatial texture of the detail, which achieves an accurate representation of the edge detail. © 2022 National Remote Sensing Bulletin. All rights reserved.     

新疆典型盐渍土微波介电特性响应分析与建模

土壤盐渍化对区域经济和生态可持续发展产生负面影响。微波介电常数是微波遥感探测土壤的关键因素,然而介电常数与盐分的关系仍不清晰。为分析盐分类型及含盐量对土壤介电常数的影响,在0.3~20.0 GHz频率下,测量了新疆典型的2种盐渍土类型（硫酸盐-氯化物型： \mathrm{N} {\mathrm{a}}_2 \mathrm{S} {\mathrm{O}}_4 - \mathrm{N} \mathrm{a} \mathrm{C} \mathrm{l};氯化物-硫酸盐型： \mathrm{N} \mathrm{a} \mathrm{C} \mathrm{l} - \mathrm{N} {\mathrm{a}}_2 \mathrm{S} {\mathrm{O}}_4）的介电常数,探讨含水量、含盐量、盐分类型及质地对土壤介电特性的影响。结果表明：（1） 含盐量对湿润土壤、干燥粉壤土的复介电常数实部（ \epsilon \mathrm{\text{'}}）和虚部（ \epsilon ″）均产生影响。（2） 对于同等级的2种湿润盐渍土在0.3 GHz频率下,整体上 \epsilon {″}_{\mathrm{N}{\mathrm{a}}_2\mathrm{S}{\mathrm{O}}_4-\mathrm{N}\mathrm{a}\mathrm{C}\mathrm{l}}> \epsilon {″}_{\mathrm{N}\mathrm{a}\mathrm{C}\mathrm{l}-\mathrm{N}{\mathrm{a}}_2\mathrm{S}{\mathrm{O}}_4}。（3） 虚部的电模量（ M ″）与含盐量的关系更紧密,且0.3~5.0 GHz是重要的频率范围。研究结果可为复杂下垫面下土壤盐渍化的微波遥感监测提供科学支持。     

Science with society: Evidence-based guidance for best practices in environmental transdisciplinary work

Transdisciplinary research is a promising approach to address sustainability challenges arising from global environmental change, as it is characterized by an iterative process that brings together actors from multiple academic fields and diverse sectors of society to engage in mutual learning with the intent to co-produce new knowledge. We present a conceptual model to guide the implementation of environmental transdisciplinary work, which we consider a “science with society” (SWS) approach, providing suggested activities to conduct throughout a seven-step process. We used a survey with 168 respondents involved in environmental transdisciplinary work worldwide to evaluate the relative importance of these activities and the skills and characteristics required to implement them successfully, with attention to how responses differed according to the gender, geographic location, and positionality of the respondents. Flexibility and collaborative spirit were the most frequently valued skills in SWS, though non-researchers tended to prioritize attributes like humility, trust, and patience over flexibility. We also explored the relative significance of barriers to successful SWS, finding insufficient time and unequal power dynamics were the two most significant barriers to successful SWS. Together with case studies of respondents’ most successful SWS projects, we create a toolbox of 20 best practices that can be used to overcome barriers and increase the societal and scientific impacts of SWS projects. Project success was perceived to be significantly higher where there was medium to high policy impact, and projects initiated by practitioners/other stakeholders had a larger proportion of high policy impact compared to projects initiated by researchers only. Communicating project results to academic audiences occurred more frequently than communicating results to practitioners or the public, despite this being ranked less important overall. We discuss how these results point to three recommendations for future SWS: 1) balancing diverse perspectives through careful partnership formation and design; 2) promoting communication, learning, and reflexivity (i.e., questioning assumptions, beliefs, and practices) to overcome conflict and power asymmetries; and 3) increasing policy impact for joint science and society benefits. Our study highlights the benefits of diversity in SWS - both in the types of people and knowledge included as well as the methods used - and the potential benefits of this approach for addressing the increasingly complex challenges arising from global environmental change.     

Simulation games as a catalyst for social learning: The case of the water-food-energy nexus game

Role-playing simulations have gained in popularity in recent years as a novel method of engaging researchers and stakeholders in a variety of social and environmental issues. While academic interest has grown on this topic, knowledge remains sparse on the underlying theories that may guide the design of such games. Thsi article introduces a new game design framework - Com­pleC­Sus (Com­plex­ity-Col­lab­o­ra­tion-Sus­tain­abil­ity) - built on the concepts of social learning and procedural rhetoric. We describe and discuss the conceptual basis for our framework, giving a detailed account of its application through the recently developed the Water–Food–Energy Nexus Game (Nexus Game) as an example. We illustrate the process involved in designing the Nexus Game through initial scoping, prototyping, and design decisions, and how game structure and debriefing have been crafted to foster social learning focused on the understanding of the underlying social-ecological system as well as fostering collaboration between stakeholders. We also provide the analysis of qualitative data collected during recent gaming sessions across three continents to evaluate the Nexus Game’s potential learning effects.     

基于数据驱动高阶学习律的轮式移动机器人轨迹跟踪控制

轮式机器人执行巡逻、播种和工业生产等任务是一个强非线性的间歇过程.针对重复运行的轮式机器人轨迹跟踪问题,本文提出了一种基于数据驱动的高阶迭代学习控制算法.首先,对轮式移动机器人的模型进行推导设计,并对推导得到的状态空间形式的离散时间模型利用基于状态转移的迭代动态线性化方法,将轮式机器人系统转化为线性输入输出数据模型;其次,设计高阶迭代优化目标函数得到控制律,并利用参数更新律估计线性输入输出数据模型中的未知参数.控制器的设计和分析只使用系统的输入输出数据,不包含任何显式的模型信息.通过采用高阶学习控制方法,在控制律中利用更多之前迭代的控制输入信息,提高了控制性能.最后,仿真结果验证了该方法在轮式机器人轨迹跟踪控制中的有效性.     

Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method北大核心CSCD

Snow avalanches,which are widely and frequently developed at high elevations,seriously threatens the built traffic corridors in the Tibetan Plateau. Susceptibility evaluation of snow avalanche via machine learning model with a high forecast accuracy can be appled to quickly and effectively assess the regional avalanche risk. This paper took the central Shaluli Mountain region as the study area,in which the snow avalanche inventory was established through remote sensing interpretation and field investigation verification. We quantitatively extracted 17 evaluation factors via GIS-based analysis,and these factors were selected through the variance expansion factor（VIF）. Four machine learning models containing SVM,DT,MLP and KNN were used to compile the susceptibility index map of snow avalanches,and kappa coefficient and ROC curve were used to verify the accuracy. The results suggested that the susceptibility indexes obtained from SVM,DT,MLP and KNN were in the range of［0,0. 964］,［0,815］,［0,0. 995］and［0,1］,respectively. The accuracy test results show that these four models all have good prediction accuracy. Among them,the SVM model is the best. The results also indicated that the areas with the high snow avalanche susceptibility mainly distributed in Genie Mountain and Rigong Mountain,most of which were above the planation surface of the Tibetan Plateau. The average altitude of the extremely high snow-avalanche-prone areas is 4 939 m,while the average altitude of the high snow avalanche-prone areas is 4 859 m. The snow avalanche has low perniciousness on the Sichuan-Tibet Highway and the Sichuan-Tibet Railway in the study area. This study can provide theoretical basis and method reference for disaster prevention and mitigation of snow avalanche along Sichuan-Tibet Railway and other major projects across Shaluli Mountains region. © 2022 Science Press (China).     

以“不变特征点集”为控制数据集的遥感图像自动化处理框架

全球变化研究需要将多种传感器光谱波长接近的图像波段一起使用,以满足遥感应用对时间分辨率和区域覆盖的要求,这给遥感图像处理提出了新要求,涉及多传感器多时相数据几何定位一致性问题、辐射归一化问题及地类属性标识一致性问题以及高度自动化处理的问题。针对上述几方面问题,提出了一个基于"不变特征点集"IFPs(Invariant Feature Points set)作为控制数据集的区域级遥感图像自动化处理框架,将图像的几何空间、辐射值空间和类别属性值空间的时空对齐问题纳入到统一框架,提供了一种间接快速处理的手段和理念,并对构建IFPs的关键技术进行了综述。     

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province,China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.     

基于支持向量机的2021年2月13日日本福岛近海Mj7.3级地震震级估算

2021年2月13日日本福岛县近海发生M_j7.3级地震,触发了日本气象厅地震预警系统,系统在首台触发后5.6s发出震级为M_j6.3级的预警第1报,首台触发后10s对公众发布警报、预警震级为M_j6.4级。基于多类型特征参数输入的机器学习支持向量机震级估算模型（SVM-M）,利用2021年2月13日日本福岛县近海M_j7.3级地震获取的日本K-net强震动观测数据,分析SVM-M模型在该次地震中首台触发初期（首台触发后1~10s）的震级估算效能。结果表明:SVM-M震级估算模型,在首台触发后1s即可给出M_j6.3级的震级估算结果,与日本气象厅在首台触发后5.6s发布的预警第1报震级相同;随着时间窗的增加,首台触发后5s和10s,SVM-M模型的震级估算结果分别是M_j6.7级和M_j6.6级,均大于日本气象厅首台触发后10s对公众发布警报的预警震级。该次地震的离线模拟结果表明:SVM-M模型可在地震发生初期有效提高地震预警震级确定的准确性和时效性。