基于TM影像的城市绿地信息提取方法研究

基于TM遥感影像,运用ERDAS,对某地城市绿地专题信息进行了提取。实验过程中首先对图像进行预处理,然后通过四种绿地信息提取方案进行比较分析,这四种方案分别为:原始波段合成法、主成份分析法、归一化植被指数(NDVI)法和实验波段组合法。将以上几种方案的图像进行反复比较,根据研究对象的实际情况,植被景观目视效果最好的是NDVI植被指数法。对以上四种方法的彩色合成图像进行监督分类,利用目视判读的方法对TM影像的分类结果进行精度检验,由此可以看出实验波段组合法的精度最高,该方法是一种有效的绿地提取的方法。     

高光谱遥感影像半监督分类研究进展

随着高光谱遥感技术的迅猛发展和应用需求的不断增加,高光谱遥感影像分类成为领域的研究热点。尽管监督学习已在高光谱遥感影像分类中取得了不错的效果,但在许多情况下,获取大规模标记样本来训练监督分类算法是困难和昂贵的。因此,利用半监督分类技术对高光谱遥感影像精准分类是一项重要的研究内容。本文首先简要介绍了高光谱遥感影像发展现状和部分应用场景。其次,本文对近年来高光谱遥感影像半监督分类研究的进展进行了综述,着重讨论了低密度分割法、生成式模型、基于分歧（差异）的方法和基于图的方法四种典型半监督分类方法的关键技术和优劣。最后,进一步讨论了半监督分类技术的潜力,为今后研究工作的优化提供思路。     

基于聚类的高光谱图像非监督波段选择方法

非监督波段选择方法是高光谱图像降维的主要方法,但现有方法应用到实际高光谱图像分类时,分类精度并不理想。本文提出一种改进的基于聚类的高光谱图像非监督波段选择方法,主要通过对传统的K-means聚类算法进行两方面改进:一方面是相似性度量函数;另一方面是聚类中心的选取。然后,通过实验数据用支持向量机法(SVM)对所提算法及现有的三种非监督波段选择方法进行分类。最后,用总体精度(OA)和Kappa系数评价分类结果。表明本文所提方法在分类精度方面优于其他现有方法。     

传统遥感影像的分类

详细讲述了传统遥感影像的分类方法,并分别介绍了监督分类和非监督分类的特点。这两种分类方法有着本质的区别,但也存在着一定的联系。     

传统遥感影像的分类

详细讲述了传统遥感影像的分类方法,并分别介绍了监督分类和非监督分类的特点。这两种分类方法有着本质的区别,但也存在着一定的联系。     

基于TM影像的广州市主城区城市绿地的提取和分析

利用遥感技术能够实现快速提取城市绿地信息,准确地计算出城市绿地面积及覆盖情况等。本文以广州市TM遥感影像为数据源,进行一系列预处理,对监督分类和先计算NDVI再采用非监督分类这两种提取方法进行比较分析。结果表明,先计算NDVI再采用非监督分类法精度较高,说明该方法是一种有效的绿地信息提取方法。     

聚类特征和SVM组合的高光谱影像半监督协同分类

本文提出了一种聚类特征和SVM组合的高光谱影像半监督协同分类方法。利用构建的协同分类框架能够将KSFCM聚类算法与半监督SVM分类器相结合,同时利用聚类和分类优势,提高分类器的分类准确率。其中,通过聚类损耗函数、分类一致函数、分类差异性、样本差异性四个指数用以构建协同分类框架,以充分利用少量类标签样本信息,避免高光谱类标签样本获取困难问题,在一定程度上解决SVM支持向量随着训练样本增加而线性增加的问题,从而寻求最佳分类结果。实验结果表明,本文所提方法得到的分类精度优于直接利用SVM进行半监督分类。     

基于BP人工神经网络的遥感影像分类

重点讨论了遥感图像分类处理过程中应用效果显著的BP神经网络方法,并在Matlab软件平台下对基于BP神经网络的分类算法进行了研究,最后将它的分类结果与ERDAS软件平台下的监督分类结果进行分类精度评定比较分析。结果表明,基于BP神经网络的遥感图像分类总精度比ERDAS软件平台下的监督分类的总精度高,是一种有效的遥感影像分类方法。     

JPEG 2000压缩对遥感影像监督分类精度的影响

遥感图像压缩会影响分类精度,是值得研究的问题。以高分辨率遥感影像(Quick Bird)的监督分类精度评定为尺度,采用ER Mapper软件的JPEG 2000图像压缩模块对图像进行压缩,再在eCognition软件中对这9种压缩比图像进行面向对象的监督分类,生成分类精度报告。通过分析分类精度的变化,研究了JPEG 2000压缩对遥感影像分类的影响程度及其在遥感影像压缩方面的应用潜力。     

三凯高速路域ZY-3影像分类方法对比研究

以贵州省三凯高速路段资源三号遥感影像为例,对比分析支持向量机监督分类、ISODATA非监督分类以及SVM面向对象分类方法的成果与精度,研究这三种分类方法在资源三号影像分类上的适用性。研究结果表明,监督分类与面向对象分类效果良好,其总体分类精度、Kappa系数等指标,都要明显优于非监督分类。对于不同地物分布以及光谱信息的资源三号影像,监督分类与面向对象分类各有优势,可以视情况选择。     

火烧迹地信息遥感快速提取方法研究

利用福建省福州地区ETM图像，在分析火烧迹地光谱特征的基础上，研究了火烧迹地信息遥感快速提取方法。文中设置了4种提取方案，即监督分类法、NDVI参与的监督分类法、NDVI差值法及谱间关系法，最后，对提取结果进行了精度评价。     

高分辨率遥感影像建筑物提取方法研究

选取了监督分类、植被指数法、支持向量机和面向对象4种方法对资源三号和高分一号影像提取建筑物信息。通过对不同方法的精度评价,探索一种最适合高分一号和资源三号影像的建筑物提取方法。     

遥感图像分类方法研究综述

 综述了遥感图像监督分类和非监督分类中的各种方法，介绍了各种方法的优缺点、适用领域和应用情况，并作了简单评述，最后，展望了遥感图像分类方法研究发展方向和研究热点。     

Visualizing uncertainty — How fuzzy logic approach can help to explore iron ore deposits?

Uncertainties in Geovisualaization / GIScience spatial data can minimize but not completely provided by the different image processing classification methods. The methods of image processing techniques are purely dependent on spectral signature values. In the present study, we collected end member spectral values from both satellite data and field signatures and applied in supervised and fuzzy classification of image processing techniques to discriminate the iron ore formations and associated land cover features of part of Godumalai hill region of Salem District, Tamil Nadu State, India. The result of analysis shows that the fuzzy classified image discriminated the iron formation with better appearance and distinct boundary between the associated features than the analyses results obtained by supervised methods.     

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing ‘optimal segmentation’. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.     

遥感影像的冰川信息提取方法对比

以林芝地区为例,首先利用2001年10-12月ETM+遥感数据、地质图和DEM数据,经过遥感图像预处理生成冰川信息提取的基础影像。在此基础上采用波段比值法、主成分分析法、光谱角制图法、非监督分类法、监督分类法等进行冰川信息的自动提取,对各种提取方法进行了对比。结果表明,监督分类提取的冰川信息图像清晰、层次分明、分类边界清楚、满足冰川地貌解译的要求。最后通过实例分析得出监督分类法在提取林芝地区冰川信息时获得了较好的应用效果。     

基于eCognition的遥感图像面向对象分类方法研究

随着高分辨率遥感图像越来越普及,传统的面向像元的图像分类方法不能满足对高分辨率遥感图像区域分类的需求,高分辨率遥感图像对图像处理的软件与硬件都有了更高的要求,因此,出现了相较于面向像元有着更高精度更为合理的面向对象分类方法,也更加适用于高分辨率遥感影像。本文通过采用面向对象分类的基本方法,运用eCognition软件,以山东省胶州市地区遥感影像为例,进行多尺度分割和面向对象分类。并用ENVI做监督分类,基于目视解译精度评定,对不同方法作出分析评价。结果表明:面向对象分类方法精度更高,更具有可靠性。     

Multiband Versus Multispectral Supervised Classification of Architectural Images

For the conservation of historic monuments, there may be considerable value in automating the methods of detection and analysis of surface condition and deterioration. This paper describes tests using a range of multiband and multispectral images for the assessment of architectural façade cover by means of supervised image classifications. From the spectral training sets, both pairwise distances (the Euclidean distance and the Jeffries-Matusita (J-M) distance) are calculated and are used to predict the a posteriori accuracy of image classification. Furthermore, the effects of increasing the number of spectral bands (blue, green, red and near-infrared) in the supervised maximum-likelihood classification procedures are also analysed, as are the benefits of applying principal components. The resultant multiband datasets increased both the J-M distance and the classification accuracy of the architectural façade, and thus enabled better identification and recognition of the different kinds of façade-cover features.     

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.