遥感影像土地利用/覆盖分类方法研究综述

从六个方面总结了国内外出现的遥感影像土地利用／覆盖分类中针对传统计算机分类方法的改进：(1)从基于统计的分类向基于非线性并行处理的人工神经网络分类、基于模糊理论为分类、基于知识的分类以硬支撑向量机等分类技术发展；(2)分类从单一利用光谱信息到利用光谱、纹理、时相、角度等多砷信息；(3)从基于像元的逐点分类到基于图斑的分类；(4)从硬分类到亚像元分类；(5)从单源遥感影像分类到利用多源遥感影像融合的分类；(6)从单分类器向复合分类器发展。     

基于知识的山东丘陵区土地利用/覆盖分类研究

土地利用/覆盖信息的获取是土地利用/覆盖变化研究的前提和基础, 传统的基于光谱信息的分类无法克服地物光谱特征相似造成的混淆。以龙口市为例, 探讨了综合应用高程、坡度等地学专家知识和地物的光谱知识, 对山东丘陵地区土地利用/覆盖进行自动分类的方法。实验证明, 基于知识的土地利用ö覆盖分类方法消除了单纯利用光谱信息的不足, 达到了90. 24% 的分类精度, 远高于最大似然法分类。     

影响ETM影像土地利用/覆盖分类精度因素的研究

训练样本量、辅助数据和分类法是影响土地利用/覆盖分类精度的3个主要因素,通过找到这3个因素的最佳组合方式以提高分类精度,分别在25%、50%、75%、100%样本量下,加入NDVI、DEM和纹理均值特征作为辅助数据,比较了分类回归树、支持向量机、最大似然法3种分类法的效果,探讨了训练样本、辅助数据以及分类技术对土地利用/覆盖分类精度的影响。结果表明：支持向量机总体分类精度较高,在相同样本量和没有有效辅助数据的情况下,SVM可以获得最佳的分类结果,总体分类精度在85%以上;在进行分类时,加入NDVI和纹理均值特征使分类回归树分类精度提高了2.82%,说明该方法对有效辅助数据的加入较为敏感;在获取的训练样本集有限而可获取有效的辅助数据时,应优先考虑利用分类回归树进行土地利用/覆盖分类。     

基于生态地理分区的5套土地利用覆盖数据的不确定性研究

基于生态地理分区从面积精度和位置精度两个方面定量探讨了5套全球土地利用/覆盖(LULC)数据产品在实际应用中的不确定性,为基于生态地理分区的相关研究选择合理数据集提供参考依据,同时为中国生产全球LULC产品提供有关信息。选择中国地区20个典型生态地理分区为研究对象,采用最小误差频率法,分析5套数据集对各个类型面积估计的不确定性大小及原因;采用混淆矩阵法,基于位置分析5套数据集在类型混分方面的不确定性大小,原因及空间分布规律。结果表明:在生态地理分区尺度,MODIS,Meris300以及Glc2000这3套数据集明显优于Umd和Usgs这两套数据集,并且随着生态地理分区自南向北\,自东向西的空间分布,这3套数据集的不确定性呈减小趋势。对所有生态地理分区而言,Meris300数据集整体估计的稳定性最高,但是估计精度不是最高,并且它对建设用地和水域的估计最有优势。MODIS数据集整体估计精度和稳定性次之,对耕地的估计最有优势。 Glc2000数据集更适用于土地利用/覆盖简单的生态地理分区。研究还发现地形和土地利用/覆盖的复杂程度是引起数据集不确定性的两个重要因素。     

基于遥感的土地利用与覆被分类系统评述及代码转换

根据国土资源部“构建国家级土地利用和覆被变化数据库及服务体系”项目的部分研究内容，简要地评述了国内外最具代表性的基于遥感数据源的土地利用与覆被分类系统——美国1：25万土地利用与土地覆被分类系统、中国科学院土地资源分类系统，并与该项目研制的基于中分辨率卫星影像的国家级土地利用与覆被分类系统进行了比较，给出了分类代码的相互转换关系，以便用户使用和查询。     

基于多特征的遥感影像土地利用/覆盖分类——以腾格里沙漠东南边缘地区为例

由于建筑物的材质不同、高楼阴影等使居民地的自动提取成为遥感技术的一个难点, 而且, 在干旱、半干旱区, 泥质房顶的居民地与泥质荒漠有异物同谱现象使居民地信息提取更加困难。准确了解防护林草的变化信息对维护沙漠铁路、公路的正常运行, 保护绿洲都有重要的意义, 而防护林草与荒漠草原因为有相同的荒漠植被类型而波谱相似, 所以在干旱、半干旱地区土地利用/ 覆盖的遥感影像分类, 普遍受到同物异谱和异物同谱现象的影响和制约, 使传统基于光谱特征的分类方法精度低。仔细分析了研究区典型地物的光谱特征和同物异谱与异物同谱现象, 并结合实地考察建立了分类体系。基于知识对监督分类的结果做后处理, 准确地提取了居民地和防护林草类型, 并对分类结果进行了精度评估。结果表明: 与传统的基于光谱特征的分类方法相比, 基于多特征的遥感影像分类精度明显提高。     

基于RS与GIS技术的川西高原土地利用/覆盖现状调查研究

选择川西高原的部分地区为研究区,以地形图、TM及ETM+影像为数据源,通过野外调查和目视解译作为信息提取手段,运用遥感技术与地理信息系统的空间数据库进行管理和空间分析,建立了研究区12个子河段的土地覆盖/利用的地形图数据库、TM/ETM+影像数据库。解译结果图形数据库和属性数据库,并通过分析确定了各个子区的土地覆盖/利用情况,研究结果表明高覆盖高寒草甸、高寒灌丛草甸、乔木林地和灌木林地是该区的主要土地覆盖类别,它们的面积占到整个研究区面积的84.14%,这也说明该地区植被覆盖较高,同时基于GIS的空间分析发现研究区植被覆盖具有较强的高度带分布特征。     

GA-based learning bias selection mechanism for real-time scheduling systems

The use of machine learning technologies in order to develop knowledge bases (KBs) for real-time scheduling (RTS) problems has produced encouraging results in recent researches. However, few researches focus on the manner of selecting proper learning biases in the early developing stage of the RTS system to enhance the generalization ability of the resulting KBs. The selected learning bias usually assumes a set of proper system features that are known in advance. Moreover, the machine learning algorithm for developing scheduling KBs is predetermined. The purpose of this study is to develop a genetic algorithm (GA)-based learning bias selection mechanism to determine an appropriate learning bias that includes the machine learning algorithm, feature subset, and learning parameters. Three machine learning algorithms are considered: the back propagation neural network (BPNN), C4.5 decision tree (DT) learning, and support vector machines (SVMs). The proposed GA-based learning bias selection mechanism can search the best machine learning algorithm and simultaneously determine the optimal subset of features and the learning parameters used to build the RTS system KBs. In terms of the accuracy of prediction of unseen data under various performance criteria, it also offers better generalization ability as compared to the case where the learning bias selection mechanism is not used. Furthermore, the proposed approach to build RTS system KBs can improve the system performance as compared to other classifier KBs under various performance criteria over a long period.     

Comparative study of supervised classification algorithms for the detection of atmospheric pollution

The management of atmospheric pollution using video is not yet widespread. However it is an efficient way to evaluate the polluting rejects coming from large industrial facilities when traditional captors are not usable. This paper presents a comparison of different classifiers for a monitoring system of polluting smokes. The data used in this work are stemming from a system of video analysis and signal processing. The database includes the pollution level of puffs of smoke defined by an expert. Six machine learning techniques are tested and compared to classify the puffs of smoke: k-nearest neighbour, naïve Bayes classifier, artificial neural network, decision tree, support vector machine and a fuzzy model. The parameters of each type of classifier are split into three categories: learned parameters, parameters determined by a first step of the experimentation, and parameters set by the programmer. We compare the results of the best classifier of each type depending on the size of the learning set. A part of the discussion concerns the robustness of the classifier facing the case where classes of interest are under-represented, as the high level of pollution in our data.     

基于CNN的吉林一号卫星城市土地覆被制图潜力评估

以吉林一号视频07B星高分遥感影像为基础,采用卷积神经网络（CNN）对城区土地覆被进行精细分类,设置多组光谱变量集合,并与最大似然法、多层感知机和支持向量机分类方法进行对比,全面评估分析各方法对城区土地覆被信息提取的适用性及波谱特征对分类精度的影响。结果表明：CNN模型的分类精度最高,总体精度高于90%,相比其他方法提高幅度达12%以上,能够显著降低“椒盐”噪音;红边波段对所有方法总体分类精度贡献十分有限,而近红外波段对分类精度的提升较为明显;总体而言,红边和近红外波段对CNN分类精度影响较为微弱。深度学习应用于吉林一号高分遥感数据能获取高精度城区土地覆被分类图,可为城市土地资源配置,城市规划与管理提供重要的支撑。     

Remote Sensing Monitoring and Spatiotemporal Pattern of Land Use/Cover Change in Built-up Area of Tianjin in the Past 70 Years

Urban land use/cover changes have an important impact on urban ecosystem services functions and the environmental quality of human settlements. This study mapped urban expansion process using the historical data, remote sensing images and urban planning maps, and acquired the urban impervious surface and green space fraction based on big data platform. We analyzed the process of Tianjin urban expansion rate, intensity and urban land cover change since 1949, and revealed the driving factors in the process of urban expansion with social economic and policy factors. The results showed that the built-up area increased from 49.15 km² in 1949 to 663.39 km² in 2015. The expansion has undergone four stages of "acceleration – deceleration – acceleration - deceleration"; the urban expansion mode presents the filled with built-up area and along with the neighboring transportation trunk lines. The proportion of green space in the built-up area is increasing, indicating that the urban ecological greening in the main urban area of Tianjin has been improved.     

基于Landsat TM数据的江苏海岸带土地利用/覆被变化检测方法比较研究

土地利用/覆被变化是全球环境变化研究的核心问题之一,选择适当的变化检测方法在江苏海岸带土地利用/覆被变化研究工作中具有重要意义。本研究选取江苏海岸带具有典型代表性的大丰市作为变化检测方法比较研究的试验区,采用1992年和2002年两期Landsat TM影像,分别运用主成分分析法、混合变化检测法和变化矢量分析法进行变化检测,提取土地利用/覆被变化信息,并对每种变化检测方法进行定量分析和综合比较。研究表明,主成分分析法对于试验区的土地利用/覆被变化研究具有检测速度快、检测精度高等优点,适合试验区以及整个江苏海岸带的土地利用/覆被变化的检测。最终,综合运用主成分分析法和分类后比较法获得了5期近30年江苏海岸带土地覆被分类图,证明了主成分分析法可有效应用于江苏海岸带的变化检测。     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.