高分辨率多光谱影像城区建筑物提取研究

城区高空间分辨率遥感数据由于存在大量同物异谱和异物同谱现象,应用传统的基于像元光谱分类的方法进行建筑物分类提取难以取得满意的效果。本文发展了一种从高分辨率Ikonos卫星影像上基于知识规则的面向对象分类提取城区建筑物方法,包括如下步骤：(1)融合1m全色和4m多光谱波段影像,生成1m分辨率的多光谱融合影像;（2）分割融合影像;（3）执行基于对象光谱的最近邻监督分类;（4）应用模糊逻辑分类器结合光谱、空间、纹理和上下文特征等知识规则进行建筑物分类。精度统计结果表明,本文提出的分类方法提取城区建筑物取得了93%的精度。     

Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area

In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images.     

山区植被类型信息提取方法研究

根据遥感图像的光谱信息和空间信息特征及不同植被的分布规律,研究利用计算机处理技术提取山区植被类型的方法。分类过程采用四个步骤完成：①均一目标的象限四分树提取分类；②多光谱数据的最小距离分类；③综合利用波谱曲线的形态和地形数据进行分类；④高程数据修正分类。在分类处理过程中,分别利用了图像的空间信息、光谱信息以及地形数据。利用该分类方法在实验小区内进行植被类型提取试验,其精度为90%.与最大似然分类方法所得结果相比较,其分类精度提高了10%.     

GF-2影像面向对象典型城区地物提取方法

国产高分遥感影像信息丰富,提供了精准的地物空间细节,深入研究高分数据处理及其提取城区地类目标信息的方法具有重要意义。本文以国产高分二号（GF-2）遥感影像为数据源,利用规则集的面向对象分类方法,通过ESP尺度分析工具选取得出最优分割尺度,建立各类地物的特征体系及分类规则,最终提取出研究区典型城区地物信息,并将之与传统基于像元的SVM监督分类结果作比较。结果表明：规则集的面向对象分类总体精度为92.23%,Kappa系数为0.9,比SVM监督分类有大幅度提高。对高分二号等高分辨率影像,面向对象的分类方法精度更高,图示效果更好,是城区地物提取的有效方法。     

基于自适应共振模型的遥感影像分类方法研究

人工神经网络（ANN）是人视觉和服的基本功能的抽象、简化和模拟。在对遥感影像的综合解释应用中，与传统的统计方法和符号逻辑方法相比较，ANN更接近人对影像的视觉解译分析过程。自适应共振理论（ART）是一种自组织产生认知编码的神经网络理论，其自组织、反馈式增量学习机能，能兼顾适应性和稳定性，克服了一般神经网络学习速度慢、网络结构难以确定、局部最小陷阱等缺陷。以FUZZY－ART和ARTMAP为基础，提出基于ART遥感影像非监督和监督分类的一般模型，并以实际上土覆盖分类和城市结构信息提取为应用实例，通过与传统统计方法和一般ANN分类器相比较，ART具有正确率更同、学习速度快、自适应性等优点，是复杂数据分类和信息提取的有效工具。     

多分类器联合监督分类方法研究

针对遥感图像监督分类方法适用范围不同且分类机制各有优劣的特点,本文提出将最大似然法与最小距离法结合的监督分类法。对eTM 影像进行分类,结果表明,与单一分类器的分类结果相比,分类器结合的监督分类技术能有效提高遥感图像专题信息提取的精度。     

A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images

Roads and buildings constitute a significant proportion of urban areas. Considerable amount of research has been done on the road and building extraction from remotely sensed imagery. However, a few of them have been concentrating on using only spectral information. This study presents a comparison between three object-based models for urban features’ classification, specifically roads and buildings, from WorldView-2 satellite imagery. The three applied algorithms are support vector machines (SVMs), nearest neighbour (NN) and proposed rule-based system. The results indicated that the proposed rules in this study, despite the spectral complexity of land cover types, performed a satisfactory output with an overall accuracy of 92.92%. The advantages offered by the proposed rules were not provided by other two applied algorithms and it revealed the highest accuracy compared to SVM and NN. The overall accuracy for SVM was 76.76%, which is almost similar to the result achieved by NN (77.3%).     

Water-Body types identification in urban areas from radarsat-2 fully polarimetric SAR data

This paper presents a novel method for supervised water-body extraction and water-body types identification from Radarsat-2 fully polarimetric (FP) synthetic aperture radar (SAR) data in complex urban areas. First, supervised water-body extraction using the Wishart classifier is performed, and the false alarms that are formed in built-up areas are removed using morphological processing methods and spatial contextual information. Then, the support vector machine (SVM), the classification and regression tree (CART), TreeBagger (TB), and random forest (RF) classifiers are introduced for water-body types (rivers, lakes, ponds) identification. In SAR images, certain other objects that are misclassified as water are also considered in water-body types identification. Several shape and polarimetric features of each candidate water-body are used for identification. Radarsat-2 PolSAR data that were acquired over Suzhou city and Dongguan city in China are used to validate the effectiveness of the proposed method, and the experimental results are evaluated at both the object and pixel levels. We compared the water-body types classification results using only shape features and the combination of shape and polarimetric features, the experimental results show that the polarimetric features can eliminate the misclassifications from certain other objects like roads to water areas, and the increasement of classification accuracy embodies at both the object and pixel levels. The experimental results show that the proposed methods can achieve satisfactory accuracies at the object level [89.4% (Suzhou), 95.53% (Dongguan)] and the pixel level [96.22% (Suzhou), 97.95% (Dongguan)] for water-body types classification, respectively.     

Detection of impervious surface change with multitemporal Landsat images in an urban–rural frontier

Mapping and monitoring impervious surface dynamic change in a complex urban-rural frontier with medium or coarse spatial resolution images is a challenge due to the mixed pixel problem and the spectral confusion between impervious surfaces and other non-vegetation land covers. This research selected Lucas do Rio Verde County in Mato Grosso State, Brazil as a case study to improve impervious surface estimation performance by the integrated use of Landsat and QuickBird images and to monitor impervious surface change by analyzing the normalized multitemporal Landsat-derived fractional impervious surfaces. This research demonstrates the importance of two-step calibrations. The first step is to calibrate the Landsat-derived fraction impervious surface values through the established regression model based on the QuickBird-derived impervious surface image in 2008. The second step is to conduct the normalization between the calibrated 2008 impervious surface image with other dates of impervious surface images. This research indicates that the per-pixel based method overestimates the impervious surface area in the urban-rural frontier by 50%-60%. In order to accurately estimate impervious surface area, it is necessary to map the fractional impervious surface image and further calibrate the estimates with high spatial resolution images. Also normalization of the multitemporal fractional impervious surface images is needed to reduce the impacts from different environmental conditions, in order to effectively detect the impervious surface dynamic change in a complex urban-rural frontier. The procedure developed in this paper for mapping and monitoring impervious surface area is especially valuable in urban-rural frontiers where multitemporal Landsat images are difficult to be used for accurately extracting impervious surface features based on traditional per-pixel based classification methods as they cannot effectively handle the mixed pixel problem.     

Multiple-entity based classification of airborne laser scanning data in urban areas

There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method.Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers.Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information.     

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.     

城市不透水面遥感估算

由于人口快速增长和农村人口向城市迁移,城市不透水面积也在持续快速增长。加速的城市扩张和无监控的城市开发会导致诸多生态环境问题。本文利用Landsat影像,采用附有限制条件的线性光谱混合分解、植被覆盖度与不透水面负相关模型、监督分类三种方法对南京城区的不透水表面分布进行空间分析。通过评估这三种方法提取的不透水面的精度和分析和种方法受其主要人为因素的影响大小以及不透水面的提取过程,表明了线性光谱混合分解方法较优。     

高光谱影像的引导滤波多尺度特征提取

为了解决高光谱遥感影像分类中单一尺度特征无法有效表达地物类间差异和区分地物边界的不足,提高影像分类精度和改善分类目视解译效果,提出了采用引导滤波提取多尺度的空间特征的方法。首先,利用主成分分析对高光谱影像进行降维,移除噪声并突出主要特征;然后,将第1主成分作为引导影像,将包含信息量最多的若干主成分分别作为输入影像,应用依次增加的滤波半径分别进行引导滤波处理提取多个尺度的特征,获得影像不同尺度的结构信息;最后,将多尺度特征输入分类器中进行影像监督分类。采用仿真数据和帕维亚大学(Pavia University)、帕维亚城区(Pavia Centre)等3幅高光谱实验数据,提取了基于引导滤波的多尺度特征、多尺度形态特征和多尺度纹理特征,输入到支持向量机、随机森林和K近邻分类器中,进行了实验。实验结果表明:采用支持向量机分类Pavia University数据,相对于采用多尺度形态特征的分类结果,引导滤波特征的总体精度提高了6.5%;Pavia Centre和Salinas两幅影像最高分类精度均由引导滤波特征实现,分别达到98.51%和98.39%。实验证实基于引导滤波提取的多尺度特征能有效地描述地物结构,进而获得更高的分类精度和改善目视解译效果。     

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.     

基于随机森林算法的城市不透水面信息提取——以长春市为例

为了快速、准确地掌握不透水面的空间分布及满足动态变化信息现实需求,本文基于多分类器集成学习的思想,引入随机森林算法,以Landsat8影像为数据源,长春市为实验区,选取光谱特征、纹理测度、空间变换后的独立分量等25个特征变量进行分类研究,根据OOB误差进行重要性分析并试验得出最优的分类模型,实现高精度不透水面信息的提取,最后与传统参数分类法进行比较。结果表明：随机森林算法的总体精度可以达到94%,高出最大似然分类法5.9%,支持向量机算法0.77%,Kappa系数为0.914 3,均方根误差为0.104 3,不透水面的提取精度达95.54%,可以精确地得出所需信息,为城市建设与规划提供有效的专题数据。     

Spatial and Quantitative Comparison of Topographically Derived Different Classification Algorithms Using AWiFS Data over Himalayas,India

In recent years, the significant increase in research on spatial information is observed. Classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier; (b) k-mean clustering as unsupervised classifier; and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5–88.5%) as compared to MLC (81–84%) and k-mean (74–81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography.     

基于误差分析的组合分类器研究

提出了一种基于误差分析的组合分类器,通过结合两种监督分类方法,提出的算法分别估计了两种监督分类方法在计算过程中的误差,给出了规则输出的置信区间,再根据置信区间的大小对两种分类方法的输出结果进行加权平均,从而得到更精确的规则输出.利用该方法对遥感图像进行分类实验,在不同训练样本分布与不同训练样本数量的情况下,比较新的组合分类器与单一分类器的精度.结果表明新的组合分类器能够取得比单一的分类器更高的分类精度.结果还显示出,两个分类器的独立性越强,组合分类器的效果越好.另外一个实验比较了新的组合分类器与和式规则组合分类器的分类精度,结果仍显示出了新方法的优越性.     

地表不透水面信息遥感的主要方法分析

全球范围内的城市扩张已使得大量的不透水面取代了以植被为主的地表自然景观,并给生态环境带来了明显的负面影响。不透水面作为一个影响生态环境的关键因子已引起了全社会的广泛关注。如何及时快速地掌握不透水面的空间分布信息,准确无误地量化不透水面的动态变化信息,是城市规划、环境保护亟待解决的现实问题。而遥感以其快速、大范围、多尺度、可重复的对地观测优势为解决这一问题提供了很好的解决方案。不透水面遥感研究经过近十几年的发展已有了长足的进步,多种针对不透水面信息反演的遥感创新技术与方法被相继提出。本文重点分析了这些针对遥感不透水面提出的创新技术,详细地指出了它们的优势和不足,并在此基础上总结了中国遥感工作者在不透水面遥感方面的研究工作。当前许多不透水面信息的反演精度都可以达到85%以上,但是不透水面与裸土和阴影信息的混淆仍是困扰不透水面信息精准反演的主要问题。由于大部分不透水面材料具有和砂土石同源的特点,因此在现有影像光谱分辨率不足的情况下,单靠光谱是很难进一步提高不透水面信息的反演精度,而借助LiDAR等其他辅助数据,将有望帮助解决这一瓶颈问题。     

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。     

利用面向对象分类方法提取冬小麦种植面积的研究

应用陆地卫星TM数据和遥感图像处理软件eCognition5.0和ENVI4.3软件,以面向对象的方法和监督分类波谱角法分别提取泰安市2005年冬小麦种植面积及其分布信息。逐像素分类的结果存在"椒盐"效应,而且很难克服同物异谱、同谱异物现象,面向对象的分类方法可以有效的集成专家知识和各种辅助数据,克服逐像素分类的弊端。分类结果表明,利用面向对象的分类方法可以获得比传统的像素级分类方法更高的分类精度,为冬小麦种植面积的自动提取提供了广阔的前景。