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%.     

基于AdaBoost算法的遥感影像水体信息提取

AdaBoost算法利用每个特征构造一个简单分类器,然后将简单分类器进行训练组合成一个强分类器。算法能够充分利用每个分类器的优势并避免其劣势,得到一个最佳判别,达到提高分类精度的目的。本文利用TM影像,将影像各波段灰度、水体指数和谱间关系特征相结合,构成提取水体的强分类器,实现水体提取。实验结果表明,算法能够非常有效地、高精度地提取水体信息。     

利用多分类器自适应级联模型的高分二号影像分类

针对传统单一分类器分类效果不够理想,存在各自的不足,以及已有的多分类器级联模型不能根据待识别样本特征进行动态调整优化等问题,提出了一种基于多分类器自适应级联模型的遥感影像分类方法。该模型选取各类别最优分类器进行级联组合,以待识别样本在整体性能最优分类器的表现对类别最优分类器作出自适应调整,对高分辨率影像分割后的像斑对象输出类别信息。以杭州区域高分二号遥感影像进行分类试验,结果表明,本文方法相比于单一分类器及已有的级联模型具有更高的分类精度。     

联合卷积神经网络与集成学习的遥感影像场景分类

针对人工设计的中、低层特征难以实现复杂场景影像的高精度分类以及卷积神经网络依赖大量训练数据等问题，结合迁移学习与集成学习，提出了一种联合卷积神经网络与集成学习的遥感影像场景分类算法。首先基于迁移学习的思想，利用在自然影像数据集上训练好的多个深层卷积神经网络模型作为特征提取器，提取图像多个高度抽象的语义特征；然后构建由Logistic回归和支持向量机组成的Stacking集成模型，对同一图像的多个特征分别训练Logistic模型，将预测概率结果融合构建概率特征；最后利用支持向量机对概率特征训练和预测，得到场景影像的分类结果。利用UCMerced_LandUse和NWPU-RESISC 45两种不同规模的遥感影像数据集进行试验，即使在只有10%的数据作为训练样本情况下，本文方法能够分别达到90.74%和87.21%的分类精度。     

Improving Spectral–spatial Classification of Hyperspectral Imagery Using Spectral Dimensionality Reduction Based on Weighted Genetic Algorithm

This paper aims to improve the accuracy and the efficiency of high resolution land cover mapping in urban area. To this end, an improved approach for classification of hyperspectral imagery is proposed and evaluated. This approach benefits from both inherent spectral and spatial information of an image. The weighted genetic (WG) algorithm is first used to obtain the subspace of hyperspectral data. The obtained features are then fed into the enhanced marker-based minimum spanning forest (EMSF) classification algorithm. In this algorithm, the markers are extracted from the classification maps obtained by both support vector machine and watershed segmentation algorithm classifiers. For this purpose, the class’s pixels with the largest population in the classification map are kept for each region of the segmentation map. Then, the most reliable classified pixels are chosen from among the exiting pixels as markers. To evaluate the efficiency of the proposed approach, three hyperspectral data sets acquired by ROSIS-03, Hymap and Hyper-Cam LWIR are used. Experimental results showed that the proposed WG–EMSF approach achieves approximately 9, 8 and 6% better overall accuracy than the original MSF-based algorithm for these data sets respectively.     

条件随机场模型约束下的遥感影像模糊C-均值聚类算法

遥感影像具有丰富的空间相关信息,而传统的基于像元光谱的聚类算法并不能将空间信息融入聚类,聚类结果往往不好。针对这一问题,本文提出了一种条件随机场模型约束下的模糊C-均值聚类算法,通过邻域像元的分类先验信息对中心像元的类别进行约束从而提取空间相关信息,基于二阶条件随机场将光谱信息和空间相关信息同时融入聚类,并使用环形置信度迭代算法得到像元分类后验概率的全局最优推测。试验证明,本文算法能够有效地保持地物的形状特征,分类精度相比传统算法有所提高。     

典型遥感数据分类方法的适用性分析——以遥感图像场景分类为例

目前普遍采用的分类器通常都是针对单一或小量任务而设计的,在小数据量的处理中能取得比较满意的结果。但对于海量遥感数据的处理,其在处理时效和分类精度方面还有待研究。本文以遥感图像场景分类任务为例,着重对遥感数据分类问题中几种典型分类方法的适用性进行比较研究,包括K近邻(KNN)、随机森林(RF),支持向量机(SVM)和稀疏表达分类器(SRC)等。分别从参数敏感性,训练样本数据量,待分类样本数据量和样本特征维数对分类器性能的影响等几个方面进行比较分析。实验结果表明:(1)KNN,RF和L0-SRC方法相比RBF-SVM,Linear-SVM和L1-SRC,受参数影响的程度更弱;(2)待分类样本固定的情况下,随着训练样本数目的增加,SRC类型分类方法的分类性能最佳,SVM类型方法次之,然后是RF和KNN,在总体分类时间上呈现出L0-SRCL1-SRCRFRBF-SVM/Linear-SVMKNN/L0-SRC-Batch的趋势;(3)训练样本固定的情况下,所有分类方法的分类精度几乎都不受待分类样本数目变化的影响,RBF-SVM方法性能最佳,其次是L1-SRC,然后是Linear-SVM,最后是RF和L0-SRC/L0-SRC-Batch,在总体分类时间上,L1-SRC和L0-SRC相比其他分类方法最为耗时;(4)样本特征维数的变化不仅影响分类器的运行效率,同时也影响其分类精度,其中SRC和KNN分类器器无需较高的特征维数即可获得较好的分类结果,SVM对高维特征具有较强的包容性和学习能力,RF分类器对特征维数增加则表现得并不敏感,特征维数的增加并不能对其分类精度的提升带来更多的贡献。总的来说,在大数据量的遥感数据分类任务中,现有分类方法具有良好的适用性,但是对于分类器的选择应当基于各自的特点和优势,结合实际应用的特点进行权衡和选择,选择参数敏感性较小,分类总体时间消耗低但分类精度相对较高的分类方法。     

利用多分类器集成进行遥感影像分类

基于信息相关理论,根据相关度值动态调整分类器的组合和权重,建立了新型的多分类器集成规则,并应用于决策树分类器、BP人工神经网络分类器和SVM分类器的集成。通过对长沙城区TM影像的分类实验发现:①三种分类器的分类结果存在较明显的差异,水体区的差异像元最少,占水体总像元的15.12%,建设用地区的差异像元最多,占建设用地区像元的54.93%;②三种分类器均具有较高的分类精度,总体精度均超过了74%,而且分类器各有优势,决策树分类器能够较好地分出水体和建设用地,BP分类器能够较好地分出水体和林地,SVM分类器对水体、林地和建设用地均有较高的分类精度;③基于全信息相关度的多分类器集成分类法明显地提升了分类结果的精度,分类精度达到了85.71%,Kappa系数达到了80.56%。     

An application of spatial filtering techniques for land cover mapping using TM‐images

Land cover classification using satellite imagery is commonly based on spectral information in the individual pixels. The information in neighbouring pixels is ignored. Spatial filtering techniques using information present in neighbouring pixels may however, contribute significantly to an improvement of the classification. In this study different methods of spatial filtering are applied to a part of a TM‐scene of Kenya to assess their relative reliability. The study area is characterized by extended, relatively homogeneous areas of eucalyptus forests and tea estates and by fragmentated areas of agricultural land use. Spectral information was combined with the results of different spatial filtering methods and then classified. The spatial filtering techniques applied were texture calculation by means of variance, “median minus original” filtering and fractal dimension computations using several sizes of templates. The obtained classification accuracy of several image combinations is compared using the percentage correctly classified and using an overall accuracy measure: the Kappa coefficient. It is concluded that in this case the spatial filtering techniques only slightly improve the classification. From the applied filtering methods texture calculation by means of variance yielded the best results.     

融合光谱-空间信息的高光谱遥感影像增量分类算法

提出了一种融合光谱和空间结构信息的高光谱遥感影像增量分类算法INC_SPEC_MPext。通过主成分分析(PCA)提取高光谱影像的若干主成分,利用数学形态学提取各主分量影像对应的形态学剖面(MP),再将所有主分量影像的形态学剖面归并联结,组成扩展的形态学剖面(MPext)。将MPext与光谱信息相结合以增加知识,最大限度地挖掘未标记样本的有用信息,优化分类器的学习能力。不断从分类器对未标记样本的预测结果中甄选置信度高的样本加入训练集,并迭代地利用扩大的训练集进行分类器构建和样本预测。以不同地表覆盖类型的AVIRIS Indian Pines和Hyperion EO-1Botswana作为测试数据,分别与基于光谱、MPext、光谱和MPext融合的分类方法进行比对。试验结果表明,在训练样本数量有限情况下,INC_SPEC_MPext算法在降低分类成本的同时,分类精度和Kappa系数都有不同程度的提高。     

Model Building to Investigate the Role of Spatial Location in Classifying Satellite Image Using SVM,CART and mBACT: A Case Study

This paper investigates the importance of spatial location of pixels in terms of row-column as an additional explanatory variable in classification along with available spectral bands of remotely sensed data. In view of this, a forward step-wise variable selection algorithm is used to select significant bands/variables and build an optimal model to extract the maximum accuracy. Author performed a case study on the area of town of Wolfville acquired by LANDSAT 5 TM data containing six 30 m resolution spectral bands and pixel location as an additional variable. Data are classified into seven classes using three advanced classifiers i.e. classification and regression trees (CART), support vector machines (SVM) and multi-class Bayesian additive classification tree (mBACT). Traditionally, it is assumed that addition of more explanatory variables always increase the accuracy of classified satellite images. However, results of this study show that adding more variables may sometimes confuse the classifier, that is, if selected carefully, fewer variables can provide the more accurate classification. Importance of row-column information turns out to be more beneficial for mBACT followed by SVM. Interestingly, spatial locations did not turn out to be useful for CART. Based on the findings of this study, mBACT appears to be a slightly better classifier than SVM and a substantially better than CART.     

遥感影像分类后处理的地统计方法

采用地统计学中带局部均值的简单克里格方法和协同克里格方法,利用基于训练样本的指示数据(位置和类别)和基于光谱初分类的类别概率数据的空间结构信息,对未知点位的类别发生概率进行预测,从而修正初分类结果。实验结果表明,两种方法所获得的精度相较初分类的精度均有明显提高,这种充分利用训练样本信息改善分类结果的策略不局限于特定的初始分类器。     

基于高分辨率遥感影像分类的地图更新方法

提出了一种在对遥感影像分类的基础上进行地图更新的方法,讨论了利用高分辨率遥感影像,通过不 同空间分辨率和光谱分辨率的影像进行融合,利用合适的高通滤波对影像进行边缘检测,构建一个三层的 MLP分类器对影像进行分类,提取城市建筑物与道路信息,并在此分类基础上通过对现有地图的叠加来实现 地图的更新。实验结果表明,基于影像融合,利用较少数量的训练样本也能生成具有较高精度的分类图,利用 分类结果图进行地图更新能取得令人满意的效果。     

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.     

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。     

面向高光谱图像分类的半监督空谱判别分析

为充分利用高光谱图像蕴藏的空间信息提升分类精度,提出了面向高光谱图像分类的半监督空谱判别分析(S3 DA)算法。考虑高光谱图像数据集的空间一致性,首先利用少量标记样本定义类内散度矩阵,保存数据集同类像元的光谱近邻结构;再利用无标记样本定义空间近邻像元散度矩阵,揭示像元间的空间近邻结构和地物的空间分布结构信息。S3 DA既保持数据集在光谱域的可分性,又保存了无标记样本蕴藏的空间域近邻结构,增强了同类像元和空间近邻像元在投影子空间的聚集性,从而提升分类性能。在PaviaU和Indian Pines数据集的试验表明,总体分类精度分别达到81.50%和71.77%。与传统的光谱方法比较,该算法能有效提升高光谱图像数据集的地物分类精度。     

高光谱-LiDAR多级融合城区地表覆盖分类

城市地区地表覆盖分类在城市研究中是一个十分重要的方向。遥感作为获取地物物理属性的一种重要技术手段,已初步应用于分类研究中。然而,随着城镇化的不断推进,城市内部地物类型越来越复杂,单一的遥感影像已无法满足城区地表覆盖分类中高精度的要求。高光谱影像和LiDAR数据能够分别表征地物的光谱信息及高程而被广泛应用。因此,根据两者之间互补的优势,本文提出了基于高光谱影像和LiDAR数据多级融合的城区地表覆盖分类方法。首先对两幅影像分别进行特征提取,将提取到的光谱、空间及高程信息进行层叠实现特征级融合。对得到的特征影像的所有像素点进行分类,然后利用LiDAR点云数据提取的建筑物掩膜,对非建筑物部分进行分类,再次实现特征级融合,以此改善建筑物区域与非建筑物区域的混淆。然后将未使用掩膜得到的分类结果与利用掩膜得到的分类结果进行投票实现决策级融合。最后利用条件随机场模型对分类结果进行后处理操作,达到平滑图像去除噪声点的目的。     

Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification

We propose a kernel nonlocal joint collaborative representation classification method based on column generation for hyperspectral imagery. The proposed approach first maps the original spectral space to a higher implicit kernel space by directly taking the similarity measures between spectral pixels as a feature, and then utilizes a nonlocal joint collaborative regression model for kernel signal reconstruction and the subsequent pixel classification. We also develop two kinds of specific radial basis function kernels for measuring the similarities. The experimental results indicate that the proposed algorithms obtain a competitive performance and outperform other state-of-the-art regression-based classifiers and the classical support vector machines classifier.     

利用面向对象的分类方法从IKONOS全色影像中提取河流和道路

传统的基于像素的统计特征的分类方法在处理高分辨率影像的分类问题上遇到了很大的困难。本研究利用面向对象的影像分析方法对IKONOS全色影像进行了河流与道路的分类,包括利用影像对象的光谱特征的初次分类和利用子目标对象的线特征的二次分类两个过程;其中初次分类由于仅依据光谱信息,不能很好地将河流或道路与其他光谱特征相似的地物区分开,而通过引入子目标的形状特征进行二次分类,就可以准确地将河流与道路提取出来。试验结果表明,面向对象的分类方法能够满足高分辨率或纹理影像的分类需要,具有很大的应用潜力。     

综合多特征的极化SAR图像随机森林分类算法

为抑制相干斑噪声对极化SAR图像分类结果的干扰,本文提出一种综合多特征的极化SAR图像随机森林分类方法。该方法首先利用简单线性迭代聚类(SLIC)算法生成超像素作为分类单元;然后,基于高维极化特征图像,利用训练好的随机森林模型,统计决策树的分类投票数,计算各超像素的类别概率;最后,利用超像素间的空间邻域特征,采用概率松弛算法(PLR)迭代修正超像素的类别后验概率,并依据最大后验概率(MAP)准则得到分类结果;实现综合利用超像素和空间邻域特征,降低相干斑噪声干扰的极化SAR图像分类方法。实验对比结果表明:本文方法能得有效抑制极化SAR图像中相干斑噪声的干扰,得到高精度且光滑连续的分类结果。