面向对象的高空间分辨率影像分类研究

采用面向对象遥感影像分类方法,进行了高空间分辨率遥感影像信息提取试验,分析了其与基于像元方法的信息提取结果的差异,试验研究表明,在目视效果上,传统方法的分类结果图中椒盐现象非常明显,而面向对象方法可以有效地避免椒盐现象;在分类精度上,面向对象方法分类结果的总体精度、Kappa系数、生产者精度、用户精度、Hellden精度和Short精度均明显高于传统方法,各类地物提取效果显著提高。面向对象方法在高空间分辨率遥感影像信息提取中具有明显的优势。     

基于面向对象的高分辨率影像专题信息提取

利用面向对象的分类方法对高分辨率影像上的水体、植被、建筑物以及道路进行了自动提取;对自动提取结果进行了精度评价:水体和植被的用户精度在90%以上,建筑物和道路中心线的用户精度在70%以上;并分析了影响信息提取精度的因素。     

遥感影像面向对象信息提取技术在GIS土地管理中的应用

本文主要研究通过高分辨率遥感影像和eCognition软件,利用面向对象信息提取技术来获取村庄尺度土地利用类型空间数据的技术方法。本项研究中的实验区主要地物包括麦田、旱作物、荒地、苗圃、道路、水体、建设用地和树木等类型。通过设置不同的分割参数并目视判定待识别地类的轮廓分割效果,获取适用于村庄尺度土地利用类型分类的最优分割参数,并通过分类精度对比说明面向对象信息提取相对于传统分类方法的巨大优势。     

一种面向对象的高分辨率遥感影像信息提取方法

针对传统面向对象分类方法的不足,根据研究对象特征构建了一种改进的面向对象的高分辨率遥感影像信息提取分类方法.首先利用SLIC超像素算法对影像进行分割,并提取分割后影像的纹理、光谱和形状特征;再利用SVM分类器提取影像信息,区分相似性较高的耕地和道路;然后利用随机森林算法提取水体和人工表面;最后对不同地物信息的提取结果进行拼接,实现土地利用分类.结果表明,与传统的面向对象分类方法相比,该方法的分类精度更高.     

面向对象分类技术在高分辨率遥感影像信息提取中的应用研究

应用高分辨率遥感数据进行矿山环境监测是近几年来矿山监测工作的一个发展趋势,信息提取技术是遥感数据应用中的关键。传统的像元分类方法只考虑了光谱信息,信息提取量少,分类精度低,难以满足高分辨率数据信息的提取。本文以IKONOS影像为数据源,利用面向对象分类新技术,探讨该技术在矿山高分辨遥感数据中的应用。最后运用kappa系数比较评价面向对象分类方法与传统的像元分类方法。研究表明,面向对象分类法比基于像元分类法精度更高,效果更好,具有较好的应用前景。     

基于面向对象的重大工程土地利用变化信息提取——以国家体育场(鸟巢)建设工程为例

以国家体育场（鸟巢）工程建设为例,研究基于面向对象的重大工程土地利用变化信息提取技术,以及建设工程对周边环境土地利用类型变化的影响。选取鸟巢2001年（动工之前）和2005年（建设中期）两期高分辨率遥感图像,运用多尺度影像分割和面向对象分类方法,进行土地利用信息提取及其变化对比分析。结果表明：利用面向对象方法取得了理想的土地利用分类结果,两期影像的分类精度分别达到93.23%和94.55%,Kappa系数分别为0.9158和0.9332;鸟巢的建设对周边土地利用类型影响较大,尤其是植被、道路和耕地的变化显著,草地和道路明显增加,耕地明显减少。     

面向对象的高分辨率卫星影像道路信息提取

本文采用面向对象方法对高分辨率卫星影像道路信息提取。首先加入建筑物矢量数据对影像分割提取出建筑物,然后采用多尺度进行分割,对分割后的对象进行最近邻采样,得到总体分类图。最后根据道路特点构建道路知识库对道路信息优化。试验表明,面向对象的道路信息提取克服了"椒盐现象",取得了较好的提取效果。     

面向对象的无人机影像分类研究

选取同处于夏收秋种时期、不同地点的3幅无人机影像进行面向对象分类。将影像在14个不同尺度上进行多尺度分割,以便获取对于分类对象较好的分割尺度。在对象基础上,将影像分为灌木、农田、道路、房屋及水体,并将分类结果精度与最大似然分类结果进行对比。结果表明,采用定量评价分割效果的方法,同时采用面向对象的方法进行信息提取,总体精度均在80%以上,明显高于最大似然分类。     

面向对象的绿地信息提取

绿地与人们的生产、生活密切相关,绿地是城市的净化器,发挥着保持水土、涵养水源、调节自然界生态平衡等重要作用.本文主要讨论了面向对象的分类技术,研究了多尺度影像分割和基于高分辨率影像的信息提取方法,在实验的基础上与传统的基于像元统计方法的信息提取结果进行了比较.结果表明,面向对象的信息提取方法在高分辨率遥感影像绿地信息提取中具有明显优势,可大大提高分类效率和精度.     

改进型算法的高分辨率遥感影像道路灾害信息提取

对灾后道路受灾信息获取,分析其交通通行能力,是救灾关键之一。本文针对传统高分辨率遥感影像道路信息提取方法效率低的问题,在面向对象算法基础上,提出了一种改进型的矢量与影像叠加的算法,对灾害道路进行信息提取。试验结果表明,该算法提高了灾害道路信息提取的效率与精度。     

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.     

Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images

This study aims to increase the accuracy of the object based classification approach to differentiate the spectrally similar land cover types to create thematic maps depicting the current land use status in rangeland. Firstly, the multispectral and panchromatic bands of a WorldView-2 MS and Pan images are fused. The fused WV-2 image is then classified with object based approach using Support Vector Machines (SVMs) classifier (Method 1). The overall classification accuracy for Method 1 is found to be 88.6%. Secondly, UAV ortho-image is utilised for segmentation process, which is required for the object based SVM classification of the WV-2 MS image (Method 2). The overall classification accuracy for Method 2 is obtained as 92.4%. It is realised that the Method 2 increases the object based classification accuracy by 4%, compared to Method 1. This result reveals that the object based classification of the UAV and WV-2 MS images makes significant contribution to the classification accuracy.     

基于面向对象和规则的遥感影像分类研究

讨论了面向对象和规则的光学遥感影像分类方法。首先利用多尺度分割形成影像对象，建立对象的层次结构，计算对象的光谱特征、几何特征、拓扑特征等，利用对象、特征形成分类规则，并通过不同对象层间信息的传递和合并实现对影像的分类。并以北京城市土地利用分类为例，对该方法进行了验证。     

土壤遥感分类识别推理决策器的设计

介绍了干旱区土壤遥感分类识别推理决策器的设计原理与实现方法。在用TM遥感图像对土壤类型进行非监督分类的基础上，建立了正向推理与逆向推理相结合的推理机制，对土壤类型进行分类识别决策。用知识表示的产生式规则与框架式规则相结合的数据结构表示土壤学专家的土壤分类识别知识。用像结构模式建立了土壤分类识别的规则，构造了土壤分类判决树，并用典型像例模式进行了各类型土壤判据文件的组织。用该方法对新疆天山北麓阜康试验区的土壤分类识别进行了试验研究。结果表明，该方法分类精度可靠，为干旱区土壤分类识别开辟了一条新的途径。     

地理信息系统支持下的山区遥感影像决策树分类

山区遥感影像分类是遥感研究的一大难题。本文利用一种决策树生成算法（C 4.5算法）自动提取知识，基于知识建立决策树用于山区影像分类，并结合研究区土地利用类型与DEM空间统计关系的先验知识，在GIS空间分析的基础上进行影像分类的后处理。与传统的最大似然法分类结果相比，该方法极大地改善了山区地表覆被分类的精度，得到试验区较为可靠的遥感分类图像。     

极化SAR参数优化与光学波谱相结合的面向对象土地覆盖分类

提出一种基于极化参数优化的面向对象分类方法。该方法结合光学和SAR数据,有效提高了对地物的识别能力。本文方法的关键在于:在■分解中,使用光学影像指导SAR影像选择同质点,使其更精确地估计极化参数并结合光学波谱信息作为输入特征;使用面向对象的分类方法,仅将光学影像作为分割输入,避免SAR噪声引起的分割错误。以美国Bakersfield地区的Sentinel-1/2数据为例,确定7种地物类型,对比分析不同输入与不同分类器对分类结果的影响。研究表明,优化输入参数在纹理丰富区域能够有效提高分类精度;面向对象的分类结果更加稳定并较好地维持地表几何特征;改进分类方法较传统分类方法总体精度提高了近10%,达到92.6%。     

Towards 3D map generation from digital aerial images

This paper describes the fusion of information extracted from multispectral digital aerial images for highly automatic 3D map generation. The proposed approach integrates spectral classification and 3D reconstruction techniques. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and near infrared (NIR) channels and form the basis for information extraction.Our land use classification is a 2-step approach that uses RGB and NIR images for an initial classification and the panchromatic images as well as a digital surface model (DSM) for a refined classification. The DSM is generated from the high resolution panchromatic images of a specific photo mission. Based on the aerial triangulation using area and feature-based points of interest the algorithms are able to generate a dense DSM by a dense image matching procedure. Afterwards a true ortho image for classification, panchromatic or color input images can be computed.In a last step specific layers for buildings and vegetation are generated and the classification is updated.     

Comparing object-based and pixel-based classifications for mapping savannas

The development of robust object-based classification methods suitable for medium to high resolution satellite imagery provides a valid alternative to ‘traditional’ pixel-based methods. This paper compares the results of an object-based classification to a supervised per-pixel classification for mapping land cover in the tropical north of the Northern Territory of Australia. The object-based approach involved segmentation of image data into objects at multiple scale levels. Objects were assigned classes using training objects and the Nearest Neighbour supervised and fuzzy classification algorithm. The supervised pixel-based classification involved the selection of training areas and a classification using the maximum likelihood classifier algorithm. Site-specific accuracy assessment using confusion matrices of both classifications were undertaken based on 256 reference sites. A comparison of the results shows a statistically significant higher overall accuracy of the object-based classification over the pixel-based classification. The incorporation of a digital elevation model (DEM) layer and associated class rules into the object-based classification produced slightly higher accuracies overall and for certain classes; however this was not statistically significant over the object-based using spectral information solely. The results indicate object-based analysis has good potential for extracting land cover information from satellite imagery captured over spatially heterogeneous land covers of tropical Australia.     

Exploitation of TerraSAR-X Data for Land use/Land Cover Analysis Using Object-Oriented Classification Approach in the African Sahel Area, Sudan

Recently, object-oriented classification techniques based on image segmentation approaches are being studied using high-resolution satellite images to extract various thematic information. In this study different types of land use/land cover (LULC) types were analysed by employing object-oriented classification approach to dual TerraSAR-X images (HH and HV polarisation) at African Sahel. For that purpose, multi-resolution segmentation (MRS) of the Definiens software was used for creating the image objects. Using the feature space optimisation (FSO) tool the attributes of the TerraSAR-X image were optimised in order to obtain the best separability among classes for the LULC mapping. The backscattering coefficients (BSC) for some classes were observed to be different for HH and HV polarisations. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value 0.82 was resulted from the classification scheme, while accuracy differences among the classes were kept minimal. Finally, the results highlighted the importance of a combine use of TerraSAR-X data and object-oriented classification approaches as a useful source of information and technique for LULC analysis in the African Sahel drylands.     

Evaluating the Performance of Multi-Class and Single-Class Classification Approaches for Mountain Agriculture Extraction Using Time-Series NDVI

Supervised multi-class classification (MCC) approach is widely being used for regional-level land use–land cover (LULC) mapping and monitoring. However, it becomes inefficient if the end user wants to map only one particular class. Therefore, an improved single-class classification (SCC) approach is required for quick and reliable map production purpose. In this regard, the current study attempts to evaluate the performance of MCC and SCC approaches for extracting mountain agriculture area using time-series normalized differential vegetation index (NDVI). At first, samples of eight LULC classes were acquired using Google Earth image, and corresponding temporal signatures (TS) were extracted from time-series NDVI to perform classification using minimum distance to mean (MDM) and spectral angle mapper (i.e., multi-class SAM—MCSAM) under MCC approach. Secondly, under SCC approach, the TS of three agriculture classes (i.e., agriculture, mixed agriculture and plantation) were utilized as a reference to extract agriculture extent using Euclidean distance (ED) and SAM (i.e., single-class SAM—SCSAM) algorithms. The area of all four maps (i.e., MDM—19.77% of total geographical area (TGA), MCSAM—21.07% of TGA, ED—15.23% of TGA, SCSAM—13.85% of TGA) was compared with reference agriculture area (14.54% of TGA) of global land cover product, and SCC-based maps were found to have close agreement. Also, the class-wise detection accuracy was evaluated using random sample point-based error matrix which reveals the better performance of ED-based map than rest three maps in terms of overall accuracy and kappa coefficient.