以地块分类为核心的冬小麦种植面积遥感估算

以提高冬小麦种植面积估算精度为目标,选取种植结构复杂的都市农业区,采用QuickBird影像数字化农田地块边界,以多时相TM影像为核心数据源,以地块为基本分类单元,进行不同特征向量组合、不同分类器的冬小麦地块分类方法研究,并对比分析了基于地块分类和基于像元分类的冬小麦种植面积估算精度。研究结果表明,基于地块分类的冬小麦种植面积估算方法的总量精度和位置精度均高于像元分类;植被指数和纹理信息的引入有助于进一步提高地块分类精度;支持向量机与最大似然均能得到高达97%的总量精度和90%的位置精度,支持向量机地块分类所需的训练样本量远低于最大似然,因此支持向量机更加适合于冬小麦地块分类;冬小麦错分与漏分情况大多发生在细碎地块,其面积总量较小,而大地块错分和漏分较少,因此相对于像元分类,地块分类能在整个区域能得到较高的冬小麦位置精度和总量精度。     

面向地块的农作物遥感分类研究进展

农作物遥感分类是农作物面积监测的核心问题,对于进一步开展农作物长势、产量等专题监测具有重要意义。与同质像元聚类得到的对象相比,地块数据包含了更为精确的位置和面积信息,被越来越多地应用于农作物遥感分类。首先,系统总结了面向地块农作物遥感分类在理论、方法和实践中取得的进展;然后,分析了该方法目前存在的问题;最后,对未来的发展趋势进行了展望。研究认为,数字化和影像分割是获取地块数据的主要途径,陆续发布的全国地块数据集也给面向地块农作物遥感分类带来了新的契机;将面向地块的农作物遥感分类策略分为考虑地块整体特征和以像元为基础2种,并总结了遥感分类特征和分类方法取得的进展;在未来一段时间,多源数据的应用、地块边界检测技术的发展、分类特征的挖掘以及遥感分类运行化能力的提高将是面向地块农作物遥感分类的重要研究内容。     

构建地块二维表征及CNN模型的作物遥感分类

本文旨在研究基于地块数据约束的深度学习模型的分类特征表示方法,以识别不同作物在不同时相上光谱差异从而对作物类型进行分类。通过Google Earth Engine平台获取作物生育期内全部Landsat 8影像,利用其质量评定波段完成研究区无云时相及区域上的地块统计,提取地块级别的各波段反射率均值按照时相顺序及波长进行排列,构建波谱、时相二维特征图作为该地块的抽象表示。通过构建相对最优的卷积神经网络CNN（Convolutional Neural Network）结构完成对特征图的分类,从而完成对地块的分类。构建CNN模型并不需要手工特征和预定义功能的需求,可完成提取特征并遵循端到端原则进行分类。将该模型的分类结果与其他最为常用机器学习分类器进行了比较,获得了优于常用遥感分类算法的分类精度。结果表明地块数据的加入可以有效的缩减计算规模并提供了准确的分类边界。所提出得方法在地块特征表示及作物分类中具有突出的应用潜力,应视为基于地块的多时相影像分类任务的优选方法。     

基于高精度历史耕地地块的农区多光谱影像分割方法研究

与传统的基于像元的影像分类方法相比,面向对象的分类方法能够提供更为准确的地类识别结果.对象作为信息提取过程的中间实体,对其划分的好坏直接关系到影像的分类精度.为了更准确地对农区多光谱影像进行分类,提出了一种基于高精度历史耕地地块数据的影像分割方法.该方法首先判定现势遥感图像上耕地地块的均质性,然后通过计算区域对比度指标...     

复合地块的拓扑类型计算与应用

按照简单地块的8种拓扑关系分类定义,计算简单地块的拓扑关系。将复合地块的外边界与空洞分离为m＋1个（m是空洞数）简单多边形,分别计算外边界之间、空洞之间、外边界与空洞之间的拓扑关系,并且按照不同的拓扑关系组合,计算复合地块的8种拓扑关系。     

自定义权重查询地块空间四至的方法及实现

基于C#语言及VS2012 IDE编译工具,采用WPF的界面,用户根据地块空间之间紧凑程度和距离,在界面自定义权重,系统根据自定义的权重计算目标地块3个指标的综合总评分值,判断某方向范围内最高分地块为当前地块的一个四至地块。程序处理过程中还包括地块外框筛选及度量标准化。     

基于多源高分卫星影像的果棉套种信息提取

棉花与果树间作在新疆多地区普遍存在,了解套种情况有利于查明果棉产量以及与常规棉田产量结构差异。为此,提出了一种综合使用多源高分遥感数据的果棉间作信息提取方法。首先,在优化分割尺度基础上分析Quick Bird卫星数据的光谱、形状和纹理特征并建立规则集;其次,使用面向对象的分类方法逐步剔除非农田信息形成地块专题图,基于专题图选择最佳纹理特征提取果树分布并以地块为单位统计套种比例;最后,依据棉花物候特征对高分一号数据多时相分类得到棉花种植信息,结合套种比例结果,统计果棉套种面积及程度。精度检验结果表明:该文提出的方法与传统抽样调查法相比能够为大量地块信息的采集节省人工成本和时间,果棉信息提取精度为89.16%,可以在统计调查工作中用于新疆果棉套种的自动化提取。     

城郊地区面向对象的多传感器影像分类

城郊地区地物类型较为复杂,利用单一传感器图像进行分类精度有限,而多传感器数据结合起来则可以"优势互补",有利于遥感影像的解译和分析。本文以武汉城郊某地块为实验对象,结合ALOS卫星三种传感器数据(包括全色、多光谱以及SAR影像)的信息,采用向对象的方法进行分类,以获取更高的分类精度。实验结果表明适当运用sar影像与光学影像结合起来进行分类,可以较好地区分出水体与阴影、水体与农作物等,对于道路、居民地等地物类型的分类也有所帮助。     

基于GIS与RS一体化的变更地块判别方法

针对土地利用动态监测面临的实际情况,提出了一种GIS与RS一体化的变更地块判别方法。该方法以矢量图斑为研究对象、以GIS的地理信息与RS的遥感影像信息为特征来源,提取图斑的特征向量。在设计识别方法时,将土地利用变更地块的识别变通成对标准地类的识别,同时引入"落入"、"误判高发区"的概念以降低变更地块的漏判率。经试验,该方法基本可替代土地利用动态监测中的人工判读工作。     

基本农田土地整理项目的内业数字化处理

基本农田土地整理项目是振兴东北老工业基地重点项目之一。从早期土地利用现状图开始,通过全数字化处理手段和软件的二次开发,阐述了全数字化内业处理的可行性和优越性,介绍了一种新的基本农田土地整理项目现状图内业处理方法,以及土地利用分类面积汇总技巧和现状图标注面积与土地利用分类地块统计面积相对应检查手段。     

作物种植成数的遥感监测精度评价

以河南开封和山西太谷地区作为研究区域 ,选用LandsatTM作为农作物种植面积遥感监测的数据源。利用LandsatTM提取河南开封实验区 2 0 0 1年的夏季作物和山西太谷地区 2 0 0 3年秋季作物的作物种植成数。同时 ,利用IKONOS ,QuickBird高分辨率遥感影像 ,通过地面调查进行了地面作物填图和分类 ,同样得到实验区的农作物种植成数。最后通过两种结果对比 ,表明开封实验区夏季作物的监测精度达到 99%以上 ,太谷实验区秋季作物的监测精度达到 97%以上 ,由此推断 ,表明利用LandsatTM监测农作物种植成数的精度能够满足中国农情遥感监测的运行化要求     

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eCognition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。     

Accurate area determination in the cadaster: case study of Slovenia

This paper discusses methodological problems of accurate area determination in the cadaster. The paper contrasts the ambiguous legal definition of the parcel boundary and parcel area in relation to the theoretically well-defined geodetic parcel boundary and the geodetic parcel area on the reference ellipsoid. To align with the real world, parcel area must account for terrain elevation. Various approximate methods for area determination which can be used in the cadaster are tested. A highly accurate method for parcel area computation is proposed, based on an equal-area projection. Considering the geodetic parcel area as a reference, the achievable accuracy of different methods is evaluated. For this analysis, the coordinates of the parcel boundary points are treated as error-free. Finally, the relevance of various systematic errors is discussed in relation to the statistical uncertainty of the parcel area, which could be gained by an a real-time kinematic GNSS survey. A case study is presented for the territory of Slovenia, its georeferencing rules, land demarcation pattern, and characteristics of its topography. Based on the results of this study, some general recommendations for the parcel area determination are given.     

光谱角法在ASTER影像土地利用分类中的应用

为了验证光谱角法(SAM)对ASTER影像分类效果,本文对SAM分类的原理进行了阐述和分析,采用了SAM方法以ASTER遥感影像数据为数据源对泸沽湖地区的土地利用进行分类研究,并对分类精度进行了分析。研究结果表明SAM方法用于ASTER数据是一种有效的分类方法,对提高ASTER影像分类精度具有重要的意义。     

An Evaluation of Four Different Wavelet Decomposition Procedures for Spatial Feature Discrimination in Urban Areas

Texture or spatial arrangement of neighborhood objects and features plays an important role in the human visual system for pattern recognition and image classification. The traditional spectral–based image processing techniques have proven inadequate for urban land use and land cover mapping from images acquired by the current generation of fine–resolution satellites. This is because of the high frequency spatial arrangements or complex nature of urban features. There is a need for an effective algorithm to digitally classify urban land use and land cover categories using high–resolution image data. Recent studies using wavelet transforms for texture analysis have generally reported better accuracy. Based on a high–resolution ATLAS image, this study illustrates four different wavelet decomposition procedures – the standard, horizontal, vertical, and diagonal decompositions – for urban land use and land cover feature extraction with the use of 33×33 pixel samples. The standard decomposition approach was found to be the most efficient approach in urban texture analysis and classification. For comparison purposes and to better evaluate the accuracy of wavelet approaches in image classification, spatial autocorrelation techniques (Moran's I and Geary's C ) and the spatial co–occurrence matrix method were also examined. The results suggest that the wavelet transform approach is superior to all other approaches.     

Dual-tree complex wavelet transform-based image enhancement for accurate long-term change assessment in coal mining areas

The main objective of this study was to improve the long-term land use change detection by improving classification accuracy of previous generation satellite image using a recent super-resolution technique. The study also analysed the change in land cover over a period of 41 years in a coal mining area. A dual-tree complex wavelet transform-based image super-resolution technique was used to enhance Landsat images of 1975 and 2016. Separating pixels with similar spectral response is an enigmatical task, especially when those pixel represent different ground features. Therefore, an advanced neural net supervised classifier was used to minimize classification errors. Accuracy of the classified images (both super-resolved and original) were measured using confusion matrices and kappa coefficients. A significant improvement of more than 10% was observed in the overall classification accuracy for the image of 1975, highlighting that the classification accuracy of earlier generation satellite data can be improved substantially.     

Classification of Satellite Images in Assessing Urban Land Use Change Using Scale Optimization in Object-Oriented Processes (A Case Study: Ardabil City,Iran)

By using satellite imagery, the recognition and evaluation of various phenomena and extraction of information necessary for the planning of land resources or other purposes are easily accomplished. The purpose of this study is to compare the efficiency of seven commonly used methods of monitored classification of satellite data to evaluate land use changes using TM and OLI Landsat, IRS, Spot5 and Quick Bird bands as well as different color combinations of these images to detect agricultural land, residential areas and aquatic areas using object-oriented processing. Digital processing of satellite images was carried out in 1998 and 2016 using advanced methods. Training samples were extracted in five user classes by eCognition software using segmentation scale optimization, different color combinations and coefficients of shape and compression. The appropriate segmentation scale for arable land, human complications and the blue areas were, respectively, 50, 8 and 10. Then each image was classified separately using seven methods and extracted samples, and efficiency of each classification method was obtained by calculating two general health and Kappa coefficients. The results show that the accuracy of each classification method and the neural network with a total accuracy of 94.475 and Kappa coefficient of 92.095 were selected as the most accurate classification method. These results show that the sampling of educational samples with proper precision of the classes in the images and dependency probability of each satellite images pixel can be useful in classifying group available in helpful area.     

Object-based land-cover classification for metropolitan Phoenix,Arizona, using aerial photography

Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1 m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural Imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. Image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land-cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time.     

单一时相遥感数据土地利用与覆盖变化自动检测方法

针对基期(用于该研究的前一时期数据)T1仅拥有土地利用和覆盖图件(矢量格式)而另一期T2拥有遥感数据的情况,构建了基于知识引导的土地利用和覆盖变化自动检测技术与方法。T1时期土地利用与覆盖与T2期遥感数据在配准叠加情况下,以T1完整的土地利用与覆盖类型图斑为单元构建土地各类别遥感数据知识库,然后以图斑单元或以像素为单位计算遥感影像特征统计量,通过与知识库相关数据的比较与匹配自动检测出变化并识别出相应的土地利用与覆盖类别。文章最后通过试验验证了该方法的有效性。