西南地区不同山地环境梯度叶面积指数遥感反演

叶面积指数(LAI)遥感估算是植被定量遥感研究的热点之一,监测植被LAI时空变化对于研究陆地生态系统碳循环及全球变化等具有非常重要的意义。在我国西南山区设置10个50km×50km的观测样区作为研究区,其中包括5个森林生态系统样区、3个农田生态系统样区和2个草地生态系统样区。分别获取不同优势植被类型LAI地面实测数据,结合同期获取的遥感数据,考虑地形因素影响,基于偏最小二乘原理分别构建各样区LAI遥感估算模型,并采用交叉验证的方式对模型精度进行评价。结果表明:考虑了海拔、坡度和坡向等地形因子的森林LAI遥感反演模型与未考虑地形变量的模型相比,其验证精度有所提高,R2由0.30~0.75提高至0.50~0.80,RMSE由0.52~0.93m2/m2降低至0.48~0.89m2/m2;所有样区优势植被类型LAI反演模型验证R2在0.40~0.80之间,RMSE在0.22~0.89m2/m2之间。发展的LAI遥感估算方法有助于认知山地植被LAI反演的地形效应问题,可为进一步的山地植被长势监测提供科学依据。     

高光谱遥感反演土壤重金属含量研究进展

土壤重金属污染是当今重要的世界性环境问题之一。如何快速高效地获取土壤重金属含量及分布信息是进行污染风险评价及预测研究的前提。高光谱遥感具有无损、实时获取信息的优势,利用高光谱技术反演土壤重金属含量仍处于探讨阶段,对其反演机理和建模方法的研究成果进行了介绍和分析。认为充分研究不同类型土壤对重金属的吸附特性,是深入理解重金属含量反演机理的基础。加强不同土壤类型、不同污染程度的案例对比分析,发展光谱信号处理方法,引入非线性建模算法,构建重金属形态量反演模型,是土壤重金属含量遥感估算研究的重要研究方向。     

Estimation of Sea Surface Velocities from SAR Images Using the Doppler Shift

The sea surface velocities field plays an important role in seawater exchange and substance transportation.In this paper,the Wide Swath Mode(WSM) data derived from the ENVISAT Advanced Synthetic Aperture Radar(ASAR) are used to retrieve the high-resolution sea surface velocities field.based on the theoretical model of SAR Doppler shift,the errors caused by the relative motion between Earth and satellite are removed.Then we use the C Band Doppler Frequency Model(CDOP) and the Bragg scattering model to remove the errors caused by the sea surface wind and Bragg scattering,respectively.The data used to verify the accuracy of the retrieval results are AVISO(Archiving Validation and Interpretation of Satellite Oceanographic) velocities and the GLD(Global Drifting Buoy) velocities.Our method is applied both in the coastal area with land cover(Agulhas) and the open sea(Kuroshio) without any land.Results show that in Agulhas,the velocity ranges from -1.8 m/s to 1.8 m/s,and their directions agree very well.In the Kuroshio,the ASAR current can clearly reveal the flow path and direction of the Kuroshio,and it matches well with the AVISO current.The comprehensive results shows,the Root Mean Square Error(RMSE) of ASAR and AVISO is 0.17 m/s,and the RMSE of ASAR and GLD is 0.11 m/s.This implies that the methods used here not only simplified the processes but also has high accuracy to retrieve sea surface velocities both in the coastal area and in the open sea.     

Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.     

面向领域的软件构件库系统初步研究

本文主要分析了面向领域应用的软件构件库系统总体框架，包括软件构件概念、软件构件库的总体结构及其相关内容。     

颜色的空间分布密度与图像检索

提出了颜色的空间分布密度的概念,给出了颜色的空间分布密度的算法,证明了算法的合理性和有效性.在基于内容的图像检索中,颜色的空间分布密度能提供颜色在空间的分布信息.实验表明,颜色的空间分布密度与颜色直方图配合使用,可以提高图像检索的准确率与回召率.     

Multi-structure information retrieval method based on transformation invariance

The needs of efficient and flexible information retrieval on multi-structural data stored in database and network are significantly growing. Especially, its flexibility plays one of the key roles to acquire relevant information desired by users in retrieval process. However, most of the existing approaches are dedicated to a single content and data structure respectively, e.g., relational database and natural text. In this work, we propose “Multi-Structure Information Retrieval” (MSIR) approach applicable to various types of contents and data structures by adapting a small part of the approach to data structures. The power of this approach comes from the use of the invariant feature information obtained from byte patterns in the files through some mathematical transformation. The experimental evaluation of the proposed approach for both artificial and real data indicates its high feasibility. Fuminori Adachi: He received his Master of engineering from Osaka University in ’03. He is enrolled in the doctoral course of Osaka University from ’03. His current research interest includes scientific discovery, data mining and machine learning techniques. Takashi Washio, Ph.D.: He received his Ph.D. from Tohoku University in ’88. In ’88, he became a visiting reseacher in Massachusetts Institute of Technology. In ’90, he joined Mitsubishi Research Institute Inc., and is working for Osaka University from ’96. His current research interest includes scientific discovery, data mining and machine learning techniques. Atsushi Fujimoto: He is enrolled in the master cource of Osaka University from ’03. His Current research interest includes correlation analysis, data mining and machine learning techniques. Hiroshi Motoda, Ph.D.: He received his Ph.D. from University of Tokyo in ’72. In ’67, he joined Hitachi Ltd. and has been working for Osaka University since ’96. His current research interest includes scientific discovery, data mining and machine learning. Hidemitsu Hanafusa: He received Master of Engineering from Keio University in ’83. In ’83, he joined The Kansai Electric Power Co. Ins. (KEPCO). He researched on Maintenance Support System at INSS from ’97 to ’02. Now, he is working in KEPCO.     

快速JPEG图像检索方法

本文给出了一种基于归一化化转动惯量(NormalizedMomentInertia,NMI)的JPEG图像快速检索方法,其特点是直接在压缩域中利用DCT系数进行块分类,每一类分块形成一个二值索引图,统计该索引图的NMI值作为该类的一个特征,所有类的NMI特征构成了图像的一个特征序列,以此进行图像检索。本方法不需要完全解压缩,降低了计算复杂度,对图像的平移,旋转和尺度变换有较好的鲁棒性。试验结果表明这种图像检索方法具有良好的检索性能。     

利用分形编码进行基于形状的图像检索

分形编码在图像压缩方面取得了很好的效果,同时也能够用于基于内容的图像检索。该文结合分形技术,提出了一种有效的抽取特征检索图像的新方法。针对目前基于形状的图像检索领域中存在的问题,首先提出了一种通过构造不规则区域的相似膨胀分形编码算法,该算法能获得较高的压缩比。在图像检索中,该算法用一新的方式提取形状特征,实现了基于该算法的图像检索操作。实验结果表明该文算法能较好地描述图像的形状及空间分布信息,明显地提高了检索效率及准确度。     

一种基于颜色块直方图的图像检索方法

提出了一种基于颜色直方图与颜色块相结合的图像检索的新方法。定义了一种新的四维图像特征矢量，它是由颜色直方图，同一种颜色的颜色块个数，同一种颜色块之间的平均距离和同一种颜色块内的平均距离组成。颜色直方图用来获取图像颜色分布的全局信息，利用后三个特征值来获取图像颜色的空间分布信息。通过对图像数据库的检索表明该算法明显优于直方图法。