改进的“基于片层—面向类”竹林信息遥感提取算法及其应用

开展复杂地形区竹林的遥感精细化识别有助于及时摸清竹林分布,充分发挥竹林的生态和经济社会价值。在“基于片层―面向类”(FB-CO)算法基础上,尝试对该算法进行改进,并利用Sentinal-2A MSI影像验证改进的有效性。改进的“基于片层―面向类”(MFB-CO)竹林信息遥感提取算法利用归一化阴影植被指数NSVI代替单波段阈值分割明亮区与阴影区林地,采用线性回归模型增强阴影区信息,并引入BPNN、SVM和RF等3种分类器提取竹林。结果表明,基于NSVI的明亮区和阴影区林地分割总精度(OA)为96.00%,优于基于NIR的83.50%;阴影信息增强后,各波段拟合模型R²均大于0.82,MRE均小于5%;FB-CO算法的竹林提取OA为82.41%,在MFB-CO算法框架下,BPNN、SVM、RF等3种分类器的竹林提取OA分别为86.51%、88.43%、88.92%,均优于FB-CO算法。由此可见,MFB-CO算法通过改进FB-CO算法几个关键步骤的具体实现途径,有效提升了竹林信息提取能力,可为竹林的精细化识别提供技术支撑。     

基于多季相Sentinel-2影像的白洋淀湿地信息提取

白洋淀湿地是华北平原上重要的浅水湖泊湿地,对雄安新区绿色发展具有重要的生态价值。对白洋淀高度异质化的景观格局进行分类,能够为白洋淀湿地资源的遥感监测提供指导意义。针对湿地季节变化的特点,对白洋淀每个季节选取一期具有代表性的Sentinel-2影像,采用分类与回归树（CART）、支持向量机（SVM）、随机森林（RF）3种常用的机器学习分类器对15种季相组合实验方案进行分类,分析不同季相遥感影像及其组合对白洋淀湿地信息提取的优劣。结果表明：相较于使用单一季相影像分类,多季相影像的组合能够显著提高分类精度,春&夏季相组合能够得到最优的分类效果,相对单季影像总体分类精度提高了10.9%~25.5%,Kappa系数提高了0.09~0.29;SVM分类器的分类表现较为稳定,能够得到最高的平均分类精度,CART分类器在处理高维特征的能力不如随机森林和SVM;不同特征类型对湿地信息提取的贡献度从高到底依次是红边光谱特征、传统光谱特征、缨帽变换特征、主成分分析特征、纹理特征。实验成果能为湿地信息的遥感识别提供依据。     

基于K型支持向量机的遥感图像分类新算法

为了提高遥感图像的分类精度和识别速度,提出了一种基于K型支持向量机（SVM）的遥感图像分类新算法,该算法将灰度共生矩阵提取的纹理特征与光谱特征相结合进行分类。对两组Landsat ETM+数据进行分类仿真实验,结果表明,在多光谱遥感图像的分类中,新算法提高了分类效率、分类精度和泛化能力,K型SVM是一种优于径向基函数SVM的分类器。     

基于混合多分类器结合算法的遥感分类?

为改善遥感影像分类精度,提出混合多分类器结合算法。考虑抽象级和测量级2个层次的特点,综合最优子分类器、Bagging算法和最大置信度区间法。应用到不同分辨率的遥感影像分类进行验证,结果表明,与选用的子分类器相比,该算法的总体精度和单个类别分类精度有明显提高,是有效的高中精度遥感影像分类算法。     

多分类器组合森林类型精细分类

针对高光谱遥感数据树种识别精度不高,现有多分类器组合策略难以避免人为因素干扰的问题,利用自适应权值模型组合2种机器学习算法,有效改善森林类型精细识别精度。研究综合利用影像的光谱和纹理特征、地形特征及森林类型外业调查样本数据,采用分层分类的策略,分别利用支撑向量机(support vector machine,SVM)和随机森林算法(random forest classifier,RFC)对森林类型进行精细识别;为进一步提高森林类型识别精度,采用自适应权值组合模型综合2种分类器,并采用分层随机抽样的独立检验样本进行精度验证。结果表明,自适应权值组合模型可综合不同分类器的优势,避免人为因素干扰且提高识别精度和稳定性,对高分五号(GF-5)星载高光谱遥感数据应用具有借鉴意义和参考价值。     

基于高分可见光遥感指数的城市阴影高效提取研究

高效识别阴影信息是利用阴影和消除阴影的关键前提,有助于城市遥感应用研究的开展,现有关于城市阴影检测多关注在近红外和可见光的多波段合成方面,而对可见光提取阴影的能力检测有待深入。针对这一问题,基于红、绿、蓝（R、G、B）高分卫星影像,结合色彩空间变换和影像多波段运算,研究并提出一种由绿光波段、蓝光波段和亮度分量构建的城市阴影优化指数OUSI(Optimization Urban Shadow Index),从视觉效果及提取精度评估角度进行验证分析。结果表明：OUSI可较完整地提取城市阴影,总体精度达90.46%,高于当前常见的指数法和深度学习阴影检测算法;OUSI受不同土地覆被类型的影响较小,阴影检测结果稳定。与既往基于特征的方法不同,研究构建的阴影指数对原始影像数据仅依赖RGB三波段信息,OUSI指数简洁有效、运算耗时少,进而可以为实现大区域和高精度的城市阴影检测提供切实可行的方案。     

基于正样本和未标记样本的遥感图像分类方法

传统分类器的构建需要正样本和负样本两类数据。在遥感影像分类中,常出现这样一类情形：感兴趣的地物只有一种。由于标记样本耗时耗力,未标记样本往往容易获取并且包含有用信息,鉴于此,提出了一种基于正样本和未标记样本的遥感图像分类方法（PUL）。首先,根据正样本固有特征并结合支持向量数据描述（SVDD）从未标记集筛选出可信正负样本,再将其从未标记集中剔除;接着将其带入SVM训练,根据未标记集在分类器中的表现设立阈值,再从未标记集中筛选出相对可靠的正负样本;最后是加权SVM（Weighted SVM）过程,初始正样本及提取出的可靠正负样本权重为1,SVM训练筛选出的样本权重范围0~1。为验证PUL的有效性,在遥感影像进行分类实验,并与单类支持向量机（OC-SVM）、高斯数据描述（GDD）、支持向量数据描述（SVDD）、有偏SVM（Biased SVM）以及多类SVM分类对比,实验结果表明PUL提高了分类效果,优于上述单类分类方法及多类SVM方法。     

云南香格里拉地区森林优势树种决策融合分类

基于Google Earth Engine（GEE）云计算平台,协同Sentinel-2影像、WordClim生物气候数据、SRTM地形数据、森林资源二类调查数据等数据,以随机森林（Random Forest, RF）,支持向量机（Support Vector Machine, SVM）和最大熵（Maximum Entropy, MaxEnt）3种机器学习算法为组件分类器,开展多源特征、多分类器决策融合的优势树种分类研究。通过3种组件分类器分别构建了两种串行集成和3种贝叶斯并行集成模型,用于确定云南香格里拉地区10种主要优势树种的空间分布。分类结果显示：3个组件分类器的总体精度均低于67.17%;3种并行集成方法总体精度相当,约为72%;两种串行集成方法精度高于78.48%,其中MaxEnt-SVM串行集成方法获得最佳精度（OA：80.66%, Kappa：0.78）,与组件分类器相比精度至少提高了13.49%。研究表明：决策融合方法在优势树种分类中比组件分类器精度更高,并且有效改善了小样本树种的分类精度,可用于大范围山区优势树种分类。     

多种分类器融合的遥感影像分类

在遥感影像分类应用中,不同分类器的分类精度是不同的,而同一分类器对不同类别的分类精度也是不相同的。多分类器结合的思想就是利用现有分类器之间的互补性,通过适当的方法将不同的分类器之间进行优势互补,往往可以得到比单个分类器更好的分类结果。本文研究了如何在Matlab下采用最短距离分类器、贝叶斯分类器、BP神经网络分类器对影像进行分类,并采用投票法进行多种分类器结合的遥感影像分类,最后进行分类后处理。实验结果表明多分类器结合的遥感影像分类比单一分类器分类的精度高。     

基于面向对象的多源卫星遥感影像玉米倒伏面积提取

风灾引起的玉米倒伏可能导致玉米大量减产,利用遥感技术准确监测玉米倒伏面积与空间分布信息对灾情的评估非常重要。利用Planet和Sentinel-2影像分别结合面向对象与基于像元方法提取研究区玉米倒伏,同时评估了不同影像特征（光谱特征、植被指数和纹理特征）与不同分类方法（支持向量机法SVM、随机森林法RF和最大似然法MLC）对玉米倒伏提取精度的影响。结果表明：①使用高空间分辨率的Planet影像进行玉米倒伏提取的精度普遍高于Sentinel-2影像;②从分类精度和面积精度来看,Planet影像的光谱特征+植被指数+均值特征结合面向对象RF分类,总体精度和Kappa系数分别为93.77%和0.87,面积的平均误差最低为4.76%;③采用Planet和Sentinel-2影像结合面向对象分类提取玉米倒伏精度高于基于像元分类。研究不仅分析了面向对象方法的优势,还评估了使用不用影像数据结合面向对象方法的适用性,可以为遥感提取作物倒伏相关研究提供一定的借鉴。     

Towards a comprehensive evaluation of V-I-S sub-pixel fractions and land surface temperature for urban land-use classification in the USA

Remote-sensing image classification based on the vegetation–impervious surface–soil (V-I-S) model and land-surface temperature (LST) has proved to be more efficient in characterizing the urban landscape than conventional spectral-based classification. However, current literature emphasizes discussion of the classifier's accuracy improvement achieved by the input of V-I-S fractions and LST over conventional spectral-based classification while ignoring the stability evaluation. Hence, this study proposes an evaluation framework for exploring the superiority of the input features and the stability of classifiers by integrating statistical randomization techniques and a kappa-error diagram. The evaluation framework was applied to case studies for demonstrating the superiority of V-I-S fractions and LST in the context of urban land-use classification with five different types of classifiers, including the maximum likelihood classifier (MLC), the tree classifier, the Bagging classifier, the random forest (RF) and the support vector machine (SVM). It followed that the use of V-I-S fractions and LST (1) could alleviate the ‘salt and pepper’ effect; (2) is preferred by tree and tree-based ensembles for branch splitting; (3) could produce classification trees with less complexity; (4) could benefit the stability of classifiers in addition to the accuracy improvement; and (5) could allow histograms following nearly normal distribution in its feature space, which boosts the performance of MLC. It is shown that MLC becomes comparable with modern classifiers when trained with V-I-S fractions and LST combination. Because of its adequacy and simplicity, MLC is recommended for urban land-use classification when V-I-S fractions and LST are used as the only input features. However, replacing them with, or including, the band reflectance might degrade MLC. A direct use of spectral band reflectance is not recommended for any of the classification approaches being considered in this study, except for SVM, which is the most robust classifier as it has a consistently high performance for all the input feature combinations. We recommend using tree-based ensemble classifiers or SVM when V-I-S fractions and LST as well as the band reflectance are all used in the classification. The proposed evaluation framework can also be applied to the assessment of input features and classifiers in other remote-sensing classification endeavours.     

Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

Mapping of patterns and spatial distribution of land-use/cover (LULC) has long been based on remotely sensed data. In the recent past, efforts to improve the reliability of LULC maps have seen a proliferation of image classification techniques. Despite these efforts, derived LULC maps are still often judged to be of insufficient quality for operational applications, due to disagreement between generated maps and reference data. In this study we sought to pursue two objectives: first, to test the new-generation multispectral RapidEye imagery classification output using machine-learning random forest (RF) and support vector machines (SVM) classifiers in a heterogeneous coastal landscape; and second, to determine the importance of different RapidEye bands on classification output. Accuracy of the derived thematic maps was assessed by computing confusion matrices of the classifiers’ cover maps with respective independent validation data sets. An overall classification accuracy of 93.07% with a kappa value of 0.92, and 91.80 with a kappa value of 0.92 was achieved using RF and SVM, respectively. In this study, RF and SVM classifiers performed comparatively similarly as demonstrated by the results of McNemer’s test (Z = 1.15). An evaluation of different RapidEye bands using the two classifiers showed that incorporation of the red-edge band has a significant effect on the overall classification accuracy in vegetation cover types. Consequently, pursuit of high classification accuracy using high-spatial resolution imagery on complex landscapes remains paramount.     

The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees

Estimating the extent of tropical rainforest types is needed for biodiversity assessment and carbon accounting. In this study, we used statistical comparisons to determine the ability of Landsat Thematic Mapper (TM) bands and spectral vegetation indices to discriminate composition and structural types. A total of 144 old-growth forest plots established in northern Costa Rica were categorized via cluster analysis and ordination. Locations for palm swamps, forest regrowth and tree plantations were also acquired, making 11 forest types for separability analysis. Forest types classified using support vector machines (SVM), a theoretically superior method for solving complex classification problems, were compared with the random forest decision tree classifier (RF). Separability comparisons demonstrate that spectral data are sensitive to differences among forest types when tree species and structural similarity is low. SVM class accuracy was 66.6% for all forest types, minimally higher than the RF classifier (65.3%). TM bands and the Normalized Difference Vegetation Index (NDVI) combined with digital elevation data notably increased accuracies for SVM (84.3%) and RF (86.7%) classifiers. Rainforest types discriminated here are typically limited to one or two categories for remote sensing classifications. Our results indicate that TM bands and ancillary data combined via machine learning algorithms can yield accurate and ecologically meaningful rainforest classifications important to national and international forest monitoring protocols.     

基于集成学习投票算法的Android恶意应用检测

针对Android平台恶意应用的检测技术,提出一种基于集成学习投票算法的Android恶意程序检测方法MASV（Soft-Voting Algorithm）,以有效地对未知应用程序进行分类。从已知开源的数据集中获取了实验的基础数据,使用的应用程序集包含213 256个良性应用程序以及18 363个恶意应用程序。使用SVM-RFE特征选择算法对特征进行降维。使用多个分类器的集合,即SVM（Support Vector Machine）、[K]-NN[（K]-Nearest Neighbor）、NB（Na?ve Bayes）、CART（Classification and Regression Tree）和RF（Random Forest）,以检测恶意应用程序和良性应用程序。使用梯度上升算法确定集成学习软投票的基分类器权重参数。实验结果表明,该方法在恶意应用程序检测中达到了99.27%的准确率。     

Diagnosis of skin cancer using machine learning techniques

Generally, skin disease is a common one in human diseases. In computer vision application, the skin color is the powerful indication for this disease. This system identifies the skin cancer disease based on the images of skin. Initially, the skin is filtered using median filter and segmented using Mean shift segmentation. Segmented images are fed as input to feature extraction. GLCM, Moment Invariants and GLRLM features are extracted in this research work. The extracted features are classified by using classification techniques like Support vector machine, Probabilistic Neural Networks and Random forest and Combined SVM+ RF classifiers. Here combined SVM+RF classifier provided better results than other classifiers.     

色多普勒超声肾动脉血流信号曲线分类研究

彩色多普勒超声是肾动脉狭窄的首选筛查工具,目前临床上主要依靠人工判别来诊断肾动脉狭窄,对操作者具有很强的依赖性。在肾动脉多普勒超声图像的基础上,通过提取肾动脉血流信号曲线、提取曲线特征,继而基于SVM构建分类器,对肾动脉血流信号曲线进行分类,取得了较高的分类精度,并与最大似然分类器进行了分类实验比较,在肾动脉狭窄的计算机辅助诊断方向进行了有意义的探索。     

Improving the SVM gender classification accuracy using clustering and incremental learning

Gender recognition has been playing a very important role in various applications such as human–computer interaction, surveillance, and security. Nonlinear support vector machines (SVMs) were investigated for the identification of gender using the Face Recognition Technology (FERET) image face database. It was shown that SVM classifiers outperform the traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, and nearest neighbour). In this context, this paper aims to improve the SVM classification accuracy in the gender classification system and propose new models for a better performance. We have evaluated different SVM learning algorithms; the SVM‐radial basis function with a 5% outlier fraction outperformed other SVM classifiers. We have examined the effectiveness of different feature selection methods. AdaBoost performs better than the other feature selection methods in selecting the most discriminating features. We have proposed two classification methods that focus on training subsets of images among the training images. Method 1 combines the outcome of different classifiers based on different image subsets, whereas method 2 is based on clustering the training data and building a classifier for each cluster. Experimental results showed that both methods have increased the classification accuracy.     

Land-cover classification of partly missing data using support vector machines

Land-cover classification based on multi-temporal satellite images for scenarios where parts of the data are missing due to, for example, clouds, snow or sensor failure has received little attention in the remote-sensing literature. The goal of this article is to introduce support vector machine (SVM) methods capable of handling missing data in land-cover classification. The novelty of this article consists of combining the powerful SVM regularization framework with a recent statistical theory of missing data, resulting in a new method where an SVM is trained for each missing data pattern, and a given incomplete test vector is classified by selecting the corresponding SVM model. The SVM classifiers are evaluated on Landsat Enhanced Thematic Mapper Plus (ETM?+?) images covering a scene of Norwegian mountain vegetation. The results show that the proposed SVM-based classifier improves the classification accuracy by 5–10% compared with single image classification. The proposed SVM classifier also outperforms recent non-parametric k-nearest neighbours (k-NN) and Parzen window density-based classifiers for incomplete data by about 3%. Moreover, since the resulting SVM classifier may easily be implemented using existing SVM libraries, we consider the new method to be an attractive choice for classification of incomplete data in remote sensing.