基于色空间转换与色域映射的跨媒体颜色传输     

SHI Rui-zhi  CAO Zhao-hui  REN Xiao-bo  YANG Lin 《测绘学院学报》2008,(2)

通过分析跨媒体彩色图像传输过程中所面临的问题,引出了彩色图像传输中的关键技术——色空间转换与色域映射技术,并在分析色空间转换与色域映射的执行机制的前提下,介绍了色域边界的确定问题,同时对色域映射中的压缩和裁切的概念与应用进行了介绍,着重论述了当前领域内比较流行的算法,如SLIN、CUSP,并对它们进行了评价。  相似文献   

Feasibility Analysis of the Use of Binary Genetic Algorithms as Importance Samplers Application to a 1-D DC Resistivity Inverse Problem     

J. P. Fernández Alvarez  J. L. Fernández Martínez  C. O. Menéndez Pérez 《Mathematical Geosciences》2008,40(4):375-408

DC resistivity inverse problems are ill-posed. For the Vertical Electrical Sounding method the acceptable solutions lie in very narrow elongated-shape regions in the model space. To characterize this ensemble of solutions is a central question. In a Bayesian framework this issue is solved adopting as solution the so-called model posterior probability distribution. However, due to the nonlinearity of the problem, this distribution is not explicitly known, or it is difficult to calculate. Therefore, algorithms that efficiently sample the model space according to it (importance sampling) are very desirable. The main goal of this paper is to numerically explore the performance of binary genetic algorithms as posterior importance sampling strategies. Their behavior will be firstly analyzed using 2D synthetic posterior test functions bearing the relevant properties of the real geo-electrical inverse problem. The conclusions will be again checked through the histogram reconstruction of parameters in a synthetic VES case, and eventually, in a real, higher dimensional, sea-water coastal intrusion problem, by comparing the results with those obtained with a theoretically correct Metropolis-Hasting importance sampler (simulated annealing without cooling). Percentile curves are introduced as an effective tool for risk assessment. We show that binary genetic algorithms perform well under very general assumptions. When the roulette wheel is the selection method used, mutation is over 10%, and the algorithm does not incorporate elitism. The results do not depend on the values of the remaining tuning parameters. Finally, to improve the efficiency of the sampling strategy, we introduce a binary genetic algorithm with oriented search space. This is done with the help of linearization of the forward operator and singular value decomposition around the maximum posterior estimate. It is shown, also, that the logarithmic model parameterization is adequate for this task.  相似文献   

超短期风速预报方法研究进展     

陈学君  李仲龙 《沙漠与绿洲气象(新疆气象)》2018,12(6):84-92

综合分析国内外风速预报相关文献,从物理方法、统计与机器学习方法、组合方法3个方面对超短期风速预报方法的研究现状进行归纳总结,对比了主要预报方法的优缺点,给出了不确定分析和误差评价指标,探讨了在超短期风速预报研究中应重点解决的问题和发展方向。  相似文献   

A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks     

Lin Wan  Zhou Huang  Xia Peng  Ran Li 《International journal of geographical information science》2018,32(11):2225-2246

Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns.  相似文献   

Fresh thing for freshman: Exploring a blended method on teaching the history of geographic thought     

Chao YE  Xinrong HUANG  Yuxin PAN  Shan YANG 《地理学报(英文版)》2018,28(12):2007-2015

It is necessary for undergraduates majoring in geography to learn the history of geographic thought. Although there are different cultural and educational backgrounds between China and the West, teaching methods such as text teaching, students’ presentations and group learning are suitable for most of teachers and students even from different countries and regions. The blended method is helpful to popularize history of geographic thought and improve the level of teaching and learning. Owing to lack of the class on the history of geographic thought in countries like China, the authors try to explore a blended method for the first-year geography undergraduates and to assess the effects of this teaching based on some questionnaires. The students have different benefits and responses to this class. A special group consisting of one teacher and several undergraduates does the research and coauthors the paper through making questionnaire, interviewing and analyzing materials from 67 freshmen majoring in human geography and geography science (teacher-training) in China. For the undergraduates especially from the countries like China, it is well worth making the history of geographic thought become a necessary and interesting class.  相似文献   

Giant Maps as Pedagogical Tools for Teaching Geography and Mathematics     

Peter Anthamatten  Lara M. P. Bryant  Beverly J. Ferrucci  Steve Jennings  Rebecca Theobald 《The Journal of geography》2018,117(5):183-192

Giant maps offer a potentially useful pedagogical tool for teaching mathematics and map skills. In this pilot study, giant maps were distributed to elementary schools in Colorado and New Hampshire and teachers were provided with guided activities designed to integrate mathematics and geography skills. In an assessment of student skills, it was found that there were improvements in map-use skills and that students improved in some skills more than others. Additionally, there were differences in gender; girls improved more in their scores for some skills and less than boys in others. The teaching benefits of giant maps merit additional investigation.  相似文献   

Facilitating Co-Production of Transdisciplinary Knowledge for Sustainability: Working with Canadian Biosphere Reserve Practitioners     

Maureen G. Reed  Paivi Abernethy 《社会与自然资源》2018,31(1):39-56

Sustainability scientists argue that diverse knowledge holders should work together in social learning processes to co-produce knowledge in support of sustainability. Yet, how to co-produce such knowledge remains unexplored. A multi-year, national knowledge sharing partnership among Canadian biosphere reserve practitioners, academic researchers and policy advisors revealed that a skilled facilitator was necessary for successful knowledge co-production. We draw attention to the multiple barriers to learning and knowledge co-production, and to the skills required of the facilitator to address them. The facilitator helped draw together local and formalized western knowledge systems (weaving) and diffuse innovations across local sites (out-scaling) and between local sites and the broader program network (up-scaling). Our experience reveals that simply bringing parties together will not generate transformative change for sustainability. Rather, multi-lateral facilitators are needed to ensure deliberate and managed interventions and to institutionalize learning across a diverse collective.  相似文献   

Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model     

耿焕同  谢博洋  葛晓燕  闵锦忠  庄潇然 《热带气象学报(英文版)》2023,29(4):482-492

Weather radar echo extrapolation plays a crucial role in weather forecasting. However, traditional weather radar echo extrapolation methods are not very accurate and do not make full use of historical data. Deep learning algorithms based on Recurrent Neural Networks also have the problem of accumulating errors. Moreover, it is difficult to obtain higher accuracy by relying on a single historical radar echo observation. Therefore, in this study, we constructed the Fusion GRU module, which leverages a cascade structure to effectively combine radar echo data and mean wind data. We also designed the Top Connection so that the model can capture the global spatial relationship to construct constraints on the predictions. Based on the Jiangsu Province dataset, we compared some models. The results show that our proposed model, Cascade Fusion Spatiotemporal Network (CFSN), improved the critical success index (CSI) by 10.7% over the baseline at the threshold of 30 dBZ. Ablation experiments further validated the effectiveness of our model. Similarly, the CSI of the complete CFSN was 0.004 higher than the suboptimal solution without the cross-attention module at the threshold of 30 dBZ.  相似文献   

路域植被等效水厚度估算模型研究     

郭云开  张晓炯  许敏  刘雨玲  钱佳  章琼 《地球信息科学学报》2020,22(2):308-315

植被等效水厚度对路域生态环境的监测评估具有重要意义。本研究以湖南醴潭高速一段为研究对象,以地面实测光谱和等效水厚度以及PRO4SAIL模拟光谱和模拟等效水厚度为数据源,利用PRO4SAIL冠层模型模拟光谱与地面实测光谱建立12种常用水分指数,引入随机森林算法对水分指数与等效水厚度进行重要性分析,得到12种水分指数的重要性排序;利用调整R ²确定建立等效水厚度估算模型中输入水分指数的最佳个数;在优选水分指数基础上,以PRO4SAIL模拟光谱计算得到水分指数和等效水厚度为训练集,分别构建随机森林耦合偏最小二乘（RF-PLS）、随机森林耦合支持向量机（RF-SVM）和随机森林耦合遗传算法优化支持向量机（RF-GA-SVM）等效水估算模型,并用地面实测等效水厚度对估算模型进行精度验证与分析。结果表明：RF-SVM估算模型中输入重要性前9的水分指数（NDWI、NMDI、SRWI、SR、NDII、WI、DWI、MSI、SAVI）时,调整R ²最高,验证集决定系数为0.8877;RF-PLS和RF-GA-SVM估算模型中输入重要性前4的水分指数（NDWI、NMDI、SRWI、SR）时,调整R ²最高,验证集决定系数分别为0.8053、0.8952,其中RF-GA-SVM模型估算等效水厚度效果最佳,其精度满足路域植被等效水厚度监测要求。本文研究成果为等效水厚度估算提供一种有效且精确的方法,同时为发展基于高光谱遥感的路域环境监测提供重要支撑。  相似文献   

基于面向对象与深度学习的榆树疏林识别方法研究   总被引：1，自引：0，他引：1  

陈昂  杨秀春  徐斌  金云翔  张文博  郭剑  邢晓语  杨东 《地球信息科学学报》2020,22(9):1897-1909

榆树疏林是浑善达克沙地中一种特殊的植被类型,它对于维持区域生态系统稳定具有重要意义,在防风固沙、涵养水源、调节气候等方面发挥着重要的作用。本文利用无人机影像与GF-2影像,对高分辨率数据源中榆树疏林的两种自动识别方法进行了研究。在面向对象方法中,首先通过计算影像对象的局部方差变化率得到了最佳分割尺度;其次采用随机森林算法对初选特征的重要性进行排序,并删除无关特征;最后分别对支持向量机（SVM）、随机森林（RF）、深度神经网络（DNN）3种分类器进行参数寻优与榆树疏林提取。此外,在ENVI5.5中基于TensorFlow框架,利用U-Net构建深度学习模型对榆树疏林进行了提取,并与面向对象方法进行对比。结果显示：① 通过面向对象方法过程的优化,最终的识别精度较以往研究有所提升,GF-2影像中SVM总体精度为90.14%,RF总体精度为 90.57%,DNN总体精度为91.14%;无人机影像中SVM总体精度为97.70%, RF与DNN总体精度为97.42%。② 深度学习方法中,GF-2影像的总体精度为91.00%,无人机影像的总体精度达到了98.43%。研究结果说明在榆树疏林提取中,无人机影像具有更高的空间分辨率,更丰富的纹理、形状等信息,能达到比GF-2影像更高的精度。面向对象方法对于2种影像都有较高的适用性;深度学习的方法在本文中更适用于无人机影像,它可以有效地减少无人机影像中的错分现象。  相似文献