基于多级决策树分类的土地利用与覆盖信息提取

结合Landsat-8遥感数据,采用多级决策树分类方案,利用归一化植被指数、波段比值、主成分分量等光谱特征参数并融合其他非遥感知识,对黄河三角洲地区土地利用与覆盖的信息展开了全面的提取、研究与分析,获得了该地区5个一级类、12个二级类地物的分布情况,分类总体精度93.88%,优于传统监督分类。同时采用聚类、分类叠加和人机交互等分类后处理操作以获得更贴近地面实际的制图效果,开展基于海岸线的缓冲区分析以获得各地物特别是距离海岸线10 km、20 km范围内地物类型的空间分布并完成相关制图与分析,为黄河三角洲地区滨海土地的利用与开发提供了数据支持。     

Kohonen神经网络在遥感影像分类中的应用研究

根据Kohonen网的生物学基础 ,基本结构和学习算法 ,提出了解决遥感影像分类的途径。依据实验区土地利用类别的光谱特征 ,采用主成分分析对遥感影像进行预处理 ,结合地理辅助数据的量化输入训练出Kohonen自组织图后对融合有地理辅助数据的影像进行土地利用分类 ,并与BP网和最大似然法分类结果进行分析比较。结果表明 ,地理辅助数据的参与对提高Kohonen网影像分类精度具有意义     

基于面向对象与规则的Sentinel-2A影像土地覆被分类——以江西省都昌县为例

土地覆被作为地表自然和人工建造物的综合体,是开展土地科学相关研究的重要基础,在遥感大数据背景下,准确、快速、自动化进行土地覆被提取技术一直是遥感研究中的重点。本文基于e Cognition软件,采用面向对象的多尺度分割法,综合考虑地物在遥感影像上的光谱、形状和纹理特征,建立多种地物提取规则。通过模糊函数、支持向量机(SVM)和阈值法对研究区的土地覆被进行分类提取,并与研究区的FROM-GLC10数据和土地利用变更数据进行了对比分析。结果表明:①研究区土地覆被分类的总体精度为97%,Kappa系数为0.96,分类精度较高;②基于10 m分辨率影像,综合使用形状、纹理、光谱信息对于道路的提取具有较好的效果,道路提取Kappa系数为0.84;③分类结果在面积和空间分布上都优于FROM-GLC10数据,与研究区实际土地变更数据保持较好的一致性。基于面向对象与规则的分类方法提取地物能够有效利用多种遥感影像特征,分类精度高,对于处理高分辨率遥感数据具有很好的优势。     

基于RS技术的城市土地利用变化研究

本文利用1999年、2008年和2017年共三期LANDSAT数据影像,采用最大似然法对其进行监督分类,并利用混淆矩阵对分类结果进行精度评判,利用监督分类结果制作研究区域土地利用分类图,对该地区土地利用数量、程度及多样性变化等情况进行综合分析,得到其土地利用类型的整体变化规律,并从多个角度分析土地利用类型变化原因,提出...     

黄土高原地形复杂区土地利用信息提取方法研究

引入影响土地利用类型分布的坡度数据和对植被覆盖反应敏感的归一化植被指数，以延河流域为研究区域，参考已有的土地利用数据，对地形复杂地区的土地利用信息提取方法进行了研究。结果表明，以监督分类和非监督分类为基础，辅以坡度信息并利用分区和多步骤信息提取方法进行土地利用信息提取，能在一定程度上提高分类精度，研究结果对建立地形复杂地区实用性强的遥感影像分类技术体系具有一定的参考价值。     

唐山市区土地利用的Landsat 8影像分层分类

针对土地利用遥感分类方法多样、分类精度高低不一等问题,该文以土地利用变化明显的唐山市路南区、路北区为研究区域,并以中分遥感影像Landsat 8OLI为信息源,在对地类样本进行可分离性分析的基础上,建立研究区土地利用分层分类体系。通过监督分类实验,选择分类效果最好、分类精度最高的最大似然分类器进行地类初分;通过绘制归一化植被指数(NDVI)、归一化建筑指数(NDBI)、两指数差值(NDVI-NDBI)的曲线及地类光谱特征曲线,建立决策树分类规则,进行地类再分。该方法可以较好地完成多种土地利用二级地类的划分,有助于提高中分影像土地利用分类效率。     

An assessment of a collaborative mapping approach for exploring land use patterns for several European metropolises

Until recently, land surveys and digital interpretation of remotely sensed imagery have been used to generate land use inventories. These techniques however, are often cumbersome and costly, allocating large amounts of technical and temporal costs. The technological advances of web 2.0 have brought a wide array of technological achievements, stimulating the participatory role in collaborative and crowd sourced mapping products. This has been fostered by GPS-enabled devices, and accessible tools that enable visual interpretation of high resolution satellite images/air photos provided in collaborative mapping projects. Such technologies offer an integrative approach to geography by means of promoting public participation and allowing accurate assessment and classification of land use as well as geographical features. OpenStreetMap (OSM) has supported the evolution of such techniques, contributing to the existence of a large inventory of spatial land use information. This paper explores the introduction of this novel participatory phenomenon for land use classification in Europe's metropolitan regions. We adopt a positivistic approach to assess comparatively the accuracy of these contributions of OSM for land use classifications in seven large European metropolitan regions. Thematic accuracy and degree of completeness of OSM data was compared to available Global Monitoring for Environment and Security Urban Atlas (GMESUA) datasets for the chosen metropolises. We further extend our findings of land use within a novel framework for geography, justifying that volunteered geographic information (VGI) sources are of great benefit for land use mapping depending on location and degree of VGI dynamism and offer a great alternative to traditional mapping techniques for metropolitan regions throughout Europe. Evaluation of several land use types at the local level suggests that a number of OSM classes (such as anthropogenic land use, agricultural and some natural environment classes) are viable alternatives for land use classification. These classes are highly accurate and can be integrated into planning decisions for stakeholders and policymakers.     

专家系统在 TM 图像分类中的应用

提高TM图像的分类精度,是图像处理及应用领域中一个很重要的研究课题。本文在总结已有成果基础上,首先利用现有的统计分类技术,对待分类图像进行预分类,并检测出“不确定”像元。然后综合光谱、地理、土壤类型、早期判别结果、目视判读经验等各种知识和信息,充分发挥专家系统的推理判断能力,对“不确定”像元的类别作进一步判别,使得整幅图像的分类精度得到改善。并据此初步建立了一个土地利用的分类系统。试验证明,这种分类方法的精度比仅用单一多光谱信息的统计分类法(最大似然法)提高约8％。     

An Automated Approach for Land Cover Classification Based on a Fuzzy Supervised Learning Framework

This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data. The land cover classification is treated as an automatically supervised learning procedure, which can be divided into automatic sample selection and fuzzy supervised classification. Once a series of features were extracted from multi-source datasets, spectral matching method is used to determine the degrees of membership of auto-selected pixels, which indicates the probability of the pixel to be distinguished as a specific land cover type. In order to make full use of this probability, a fuzzy support vector machine (SVM) classification method is used to handle samples with membership degrees. This method is applied to Landsat Thematic Mapper (TM) data of two areas located in Northern China. The automatic classification results are compared with visual interpretation. Experimental results show that the proposed method classifies the remote sensing data with a competitive and stable accuracy, and demonstrate that an objective land cover classification result is achievable by combining several advanced machine learning methods.     

基于自然间断点分级法的土地利用数据网格化分析

土地利用在自然资源统一管理中扮演着重要角色,面对不同区域和年份的数据,统一分析比对口径尤为重要,同时也应反映出相互之间的差异。本文以宜兴市2009年和2017年土地利用现状数据为数据源,首先使用统一的分类标准提取用地类型中的3大类,通过不同大小的单元划分尝试和结果分析,发现适用于该数据的网格尺度大小;然后基于自然间断点分级法进行分级范围划定,对宜兴市三类用地类型的分布和变化趋势进行综合分析,较为真实地反映了宜兴市用地情况;最后通过选用合适的空间尺度和分级范围划定方法,进而构建一个兼具操作性和科学性的土地利用数据网格化方法,为自然资源部门统筹管理和综合治理提供依据。     

K-L变换与NDBI指数法提取ASTER 影像城市用地信息的比较

运用K-L变换和NDBI(Normalized Difference Barren Index)指数法,对试验区--沧州市及其周边地区的ASTER遥感影像进行处理,然后分别对两种方法处理后的图像采用最小距离法监督分类,提取城市用地信息,并对分类后的图像进行对比,结果表明:NDBI指数法对城市用地信息提取的效果较好.     

基于Landsat-8 OLI的西安市土地利用类型遥感分类研究

近年来,由于区域人口的增加和社会经济的快速发展,西安市的土地利用类型发生了明显变化。土地利用分类可为生态系统模型、水资源模型和气候模型等提供重要信息,遥感技术为土地利用分类提供了有效的工具。本文以西安市2016年Landsat-8卫星的OLI多光谱数据为基础资料,参考国家土地利用分类标准和西安市土地利用现状,将西安市的土地类型分为建设用地、裸地、水体、草地、耕地、林地6类,采用监督分类中常用的最大似然分类法和决策树分类方法对研究数据进行解译,利用总体分类精度和Kappa系数等指标对各分类精度加以评价,并结合实际用地情况对分类结果进行了总结分析。     

浅谈第二次土地调查地类调查

第二次土地调查的土地分类与已有的土地分类不同,在调查中对土地分类的判定方法有偏差就会在土地详查、土地利用更新调查中产生差异。因此,如何准确、合理地判定每块土地的分类,对全面查清土地利用状况,掌握真实的土地基础数据十分重要。本文在分析《第二次全国土地调查技术规程》的土地分类基础上,结合辽宁省实践提出了一些想法,为开展第二次土地调查进行地类调查提供参考。     

基于RS与GIS的固阳县土地利用变化研究

采用单一遥感影像和单纯的监督分类方法,在土地利用调查中,难以获得高精度的土地利用变化数据。为解决此问题,笔者以包头固阳县为研究区,利用主成分变换的方法,对多源遥感影像（ETM＋多光谱数据和中巴资源二号卫星全色波段数据）进行融合处理;同时,在分类中,采用监督分类辅以目视解译分类法相结合的混合分类法,改进训练样本选取方法,进行变化信息的提取,同时给出了易混淆地物的遥感解译标志。此方法的使用,使土地利用信息自动提取的精度明显提高,得到的总体分类精度为82．13％,Kappa指数值0．8016。研究结果为包头市固阳县土地利用变化动态监测,提供了重要的技术支持和借鉴。研究中使用了中巴资源二号卫星影像,取得了一定的成效,这在国内土地利用变化影像应用研究方面还较新,需进一步的研究,同时由于此影像的免费使用,使得成本费用大大降低,值得大力推广。     

Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach

An attempt has been made to understand the potential of temporal Advanced Wide Field Sensor (AWiFS) data aboard IRS-P6 (Resourcesat) to generate the land use land cover information along with the net sown area. The temporal data sets were georeferenced, converted to top of atmosphere reflectance and classified using decision tree classifier, See5. Results indicate that the temporal data set could give a better definition of training sites thereby resulting in good overall kappa (kappa = 0.8651) as well as individual classification accuracies. However, co-registration of temporal datasets accuracies also has got a significant influence on the classification accuracy. Temporal variation in cloud infestation and availability of appropriate data sets within the season (before harvest of the crop) has also affected the classification accuracy.     

基于TM影像的北京城乡结合部土地利用变化分析

利用遥感分类技术能够快速获取土地利用变化信息。基于1996年和2006年两时相的北京城乡结合部地区TM卫星影像数据,采用监督分类和分类后处理方法,对研究区10年间的土地利用变化情况进行了详细分析,得到如下结论:10年间北京城南地区城乡结合部的各种土地利用类型之间相互转化,并以耕地,林地和建设用地相互转化最为显著;耕地和大范围水域面积较大幅度减少,城市居民点及工矿用地和未利用土地面积大幅度地增加,城乡结合部的范围在10年间从北向南进行了大范围地移动。     

用于土地利用分类的计算机复合分层分类方法——河北省南皮县土地利用分类研究

本文介绍了遥感图像的计算机复合分层分类方法:在用马氏距离判决分类基础上,引入了土壤图、地形图和纹理结构信息以及专家知识,对初始分类结果进行了分层判决。提高了分类精度。     

Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity

Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines – SVM), and hybrid (unsupervised–supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different depending on land use/cover classes. Early-growth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land use/cover classes were mapped with producer's and user's accuracies of ∼90%. Our classification approach seems very well suited to accurately map land use/cover of heterogeneous landscapes, thus having great potential to contribute to climate change mitigation schemes, conservation initiatives, and the design of management plans and rural development policies.     

NWFE结合纹理特征的SVM土地覆被分类方法研究

为提高土地覆被分类精度,采用非参数权重特征提取(nonparametric weighted feature extraction,NWFE)结合纹理特征的支持向量机(support vector machines,SVM)的分类法,对新疆玛纳斯河流域绿洲区2006年的土地覆被进行分类,并将该方法与主成分分析(principal component analysis,PCA)结合纹理特征的SVM分类、原始波段结合纹理特征的SVM分类进行对比。结果表明,NWFE结合纹理特征的SVM分类结果优于其他2种分类结果,不仅反映了土地覆被分布的整体情况,而且使不同土地覆被类型得到较好的区分,总体分类精度达89.17%。     

近十二年兰州市土地利用变化的动态监测

为了加强兰州市对土地类型和土地利用变化的监测,本文在ENVI和ArcGIS平台上,基于最大似然分类法,利用Landsat TM和ETM+卫星遥感影像实现了兰州市的土地利用分类,然后据此生成地物类别转移矩阵。最后对1999~2011年的土地利用/土地覆盖从范围和转变的类型进行了时空上的动态分析。