Combining QuickBird,LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees.     

Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels

The prospect of regular assessments of insect defoliation using remote sensing technologies has increased in recent years through advances in the understanding of the spectral reflectance properties of vegetation. The aim of the present study was to evaluate the ability of the red edge channel of Rapideye imagery to discriminate different levels of insect defoliation in an African savanna by comparing the results of obtained from two classifiers. Random Forest and Support vector machine classification algorithms were applied using different sets of spectral analysis involving the red edge band. Results show that the integration of information from red edge increases classification accuracy of insect defoliation levels in all analysis performed in the study. For instance, when all the 5 bands of Rapideye imagery were used for classification, the overall accuracies increases about 19% and 21% for SVM and RF, respectively, as opposed to when the red edge channel was excluded. We also found out that the normalized difference red-edge index yielded a better accuracy result than normalized difference vegetation index. We conclude that the red-edge channel of relatively affordable and readily available high-resolution multispectral satellite data such as Rapideye has the potential to considerably improve insect defoliation classification especially in sub-Saharan Africa where data availability is limited.     

基于SVM的光学遥感影像分类与评价

探求适合遥感影像分类的方法是遥感影像应用研究的重点。深入研究了支持向量机(supportvectormachine,SVM)理论和算法,用无人机影像和Landsat8OLI/TIRS影像进行试验,计算分类后总体精度和Kappa系数。结果显示,SVM应用于光学遥感图像分类精度高,提取轮廓更完整。     

关联向量机在高光谱影像分类中的应用

将关联向量机应用于高光谱影像分类, 实现高维空间中训练样本不足时分类器的精确建模。从稀疏贝叶斯理论出发, 分析关联向量机原理, 探讨一对多、一对一和两种直接的多分类方法。实验环节比较了各种多分类方法, 并从精度、稀疏性两方面将关联向量机与支持向量机等经典算法比较。实验结果表明, 两种直接的多分类方法内存占用大、效率低; 一对多精度最高, 但效率较低; 一对一计算效率最高, 精度与一对多近似。关联向量机精度不如支持向量机, 但解更稀疏, 测试样本较多时实时性好, 适合处理大场景高光谱影像的分类问题。     

高光谱遥感影像支持向量机分类中噪声影响的研究

为了验证噪声对支持向量机分类器性能的影响,对"SVM可以有效用于含噪声和不确定性数据"这一观点进行定量分析评价,采用国产OMISII传感器获得的高光谱遥感数据进行了试验,为了更好地比较SVM分类器的抗噪性,先对原始数据进行支持向量机分类,然后在高光谱遥感影像中人为添加不同比例的椒盐噪声和条带噪声,然后进行支持向量机分类...     

特征维数对支持向量机分类器性能影响的研究——以高光谱遥感影像为例

本文为验证SVM对高维特征的适应性和可靠性,针对不同特征提取方法与特征组合,以国产OMISⅡ传感器获得的北京昌平地区高光谱遥感据为例,对SVM分类器中特征维数对分类准确率的影响进行了试验,通过对主成分分析、最小噪声分离算法、相关系数分组后特征提取、导数光谱等的分析,表明SVM分类器的分类精度随着特征维数波动,其中主成分分析降维后提取的特征具有用于分类能够获得最高的准确率。通过与最大似然法和光谱角制图分类算法的比较,说明在同样的特征输入情况下SVM分类算法分类的准确率高于最大似然法和光谱角制图分类器。     

利用分离性测度多类支持向量机进行高光谱遥感影像分类

从支持向量机的基本理论出发,结合高光谱数据的分离性测度,提出了一种基于分离性测度的二叉树多类支持向量机分类器,并用OMIS传感器获得的高光谱遥感数据和Hyperion高光谱遥感数据进行实验,分析比较了各种多类SVM的分类精度,并和传统的光谱角制图和最小距离分类算法进行了比较。结果表明,SVM进行高光谱分类时,基于分离性测度的二叉树多支持向量机的分类精度最高。     

Integration of SAR-derived river inundation areas,high-precision topographic data and a river flow model toward near real-time flood management

Since several space-borne synthetic aperture radar (SAR) instruments providing high spatial resolutions and multi-polarisation capabilities will be mounted on satellites to be launched from 2006 onwards, radar imagery promises to become an indispensable asset for many environmental monitoring applications. Due to its all weather, day and night capabilities, SAR imagery presents obvious advantages over optical instruments, especially in flood management applications. To date, however, the coarse spatial resolution of available SAR datasets restricts the information that can be reliably extracted and processing techniques tend to be limited to binary floodplain segmentation into ‘flooded’ and ‘non flooded’ areas. It is the purpose of this paper to further improve the exploitation of SAR images in hydraulic modelling and near real-time crisis management by means of developing image processing methodologies that allow for the extraction of water levels at any point of the floodplain. As high-precision digital elevation models (DEM) produced, for instance, from airborne laser scanning become more readily available, methods can be exploited that combine SAR-derived flood extent maps and precise topographic data for retrieving water depth maps. In a case study of a well-documented flood event in January 2003 on the River Alzette, Grand Duchy of Luxembourg, a root mean squared error (R.M.S.E.) of 41 cm was obtained by comparing the SAR-derived water heights with surveyed high water marks that were collected during image acquisition. Water levels that were computed by a previously calibrated hydraulic model also suggest that the water surface profiles provided by the combined use of topographic data and SAR accurately reflect the true water line. The extraction of flooded areas within vegetated areas further demonstrates the usefulness of the proposed methodology.     

An innovative support vector machine based method for contextual image classification

Several remote sensing studies have adopted the Support Vector Machine (SVM) method for image classification. Although the original formulation of the SVM method does not incorporate contextual information, there are different proposals to incorporate this type of information into it. Usually, these proposals modify the SVM training phase or make an integration of SVM classifications using stochastic models. This study presents a new perspective on the development of contextual SVMs. The main concept of this proposed method is to use the contextual information to displace the separation hyperplane, initially defined by the traditional SVM. This displaced hyperplane could cause a change of the class initially assigned to the pixel. To evaluate the classification effectiveness of the proposed method a case study is presented comparing the results with the standard SVM and the SVM post-processed by the mode (majority) filter. An ALOS/PALSAR image, PLR mode, acquired over an Amazon area was used in the experiment. Considering the inner area of test sites, the accuracy results obtained by the proposed method is better than SVM and similar to SVM post-processed by the mode filter. The proposed method, however, produces better results than mode post-processed SVM when considering the classification near the edges between regions. One drawback of the method is the computational cost of the proposed method is significantly greater than the compared methods.     

高光谱遥感影像分类研究进展

随着模式识别、机器学习、遥感技术等相关学科领域的发展,高光谱遥感影像分类研究取得快速进展。本文系统总结和评述了当前高光谱遥感影像分类的相关研究进展,在总结分类策略的基础上,重点从以核方法为代表的新型分类器设计、特征挖掘、空间-光谱分类、基于主动学习和半监督学习的分类、基于稀疏表达的分类、多分类器集成六个方面对高光谱影像像素级分类最新研究进行了综述。针对今后的研究方向,指出高光谱遥感影像分类一方面要适应大数据、智能化高光谱对地观测的发展前沿,继续引入机器学习领域的新理论、新方法,综合利用多源遥感数据、多维特征空间互补的优势,提高分类精度、分类器泛化能力和自动化程度;另一方面要关注高光谱遥感应用的需求,突出高光谱遥感记录精细光谱特征的优势,针对应用需求发展有效的分类方法。     

一种基于支撑向量机的遥感影像不完全监督分类新方法

不完全监督分类是研究在只有目标类训练样本的情况下如何准确地将目标类从数据集中提取出来。在许多遥感应用问题中,往往只需要从遥感影像中提取某一类地物。如果分类过程中只要选取目标类训练样本,将节省在训练样本选取过程中的大量人力物力。因此不完全监督分类是一个值得研究的遥感分类问题。提出了一种基于加权无标识样本支撑向量机(WUS-SVM),并在其基础发展出一种不完全监督分类方法。该方法分3个步骤:(1)在影像中随机选取一定量的无标识样本,将它们作为具有不同权重的非目标类训练样本;(2)用目标类的训练样本和加权无标识训练样本一起训练WUS-SVM,得到初步的分类器;(3)利用初步的分类器确定无标识样本的类别,并与原目标类训练样本一起再次训练SVM得到最终的分类器。通过对模拟数据和遥感影像的分类试验初步证明了该分类方法的有效性。     

改进的ELU卷积神经网络在SAR图像舰船检测中的应用

随着航天技术的发展,我国SAR载荷的探测体系呈现多种类、多分辨率的发展趋势。传统的检测识别方法很难适应多分辨率、多种类的SAR图像数据,从而需要寻求一种能从多分辨率的图像数据中提取有效特征的方法。智能化发展非常迅速,本文基于SAR图像的特点,提出了改进的ELU激活函数卷积神经网络的方法,建立了结合ELU激活函数和二次代价函数的深度学习模型。同时,在训练样本中建立样本特征与所在分类中心的距离函数,用模糊支持向量机（FSVM）对提取的特征进行了分类。试验结果表明,本文方法提高了SAR图像舰船检测的抗噪性,并且检测率达到了98.6%。     

高光谱遥感影像SVM分类中训练样本选择的研究

支持向量机(SVM)分类的关键是发现分类最优超平面及类别间隔,而混合像元比纯净像元更接近类别边界,更容易找出最优超平面。本文针对SVM分类器的特点,在高光谱数据分类中采用混合像元作为训练样本对SVM进行训练,试验表明采用类别边界上的混合像元作为训练样本是可行的,能够获得与纯净训练样本接近的分类精度,进一步验证了SVM分类对训练样本空间分布依赖度较低的特点。     

顾及分类器参数的全极化SAR图像特征选择与分类

全极化SAR获取的信息量远多于传统SAR,但信息量的增加并不能确保分类精度的提高,如何有效进行特征选择至关重要。针对自适应特征选择问题,提出一种顾及分类器参数的特征选择和分类方法。该方法以支持向量数为评估依据,结合遗传算法进行特征选择,并同时对分类器参数进行寻优;最后利用优选的特征集和模型参数进行分类。为验证算法的有效性,利用两组全极化数据进行了监督分类实验。实验结果表明,提出方法降低了SVM分类器对自身参数的敏感性,而且能在较少特征个数下具备良好的泛化性能,分类精度优于未经过特征选择和参数优化的方法。     

Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models

Land cover monitoring using digital Earth data requires robust classification methods that allow the accurate mapping of complex land cover categories. This paper discusses the crucial issues related to the application of different up-to-date machine learning classifiers: classification trees (CT), artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). The analysis of the statistical significance of the differences between the performance of these algorithms, as well as sensitivity to data set size reduction and noise were also analysed. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land cover categories in south Spain. Overall, statistically similar accuracies of over 91% were obtained for ANN, SVM and RF. However, the findings of this study show differences in the accuracy of the classifiers, being RF the most accurate classifier with a very simple parameterization. SVM, followed by RF, was the most robust classifier to noise and data reduction. Significant differences in their performances were only reached for thresholds of noise and data reduction greater than 20% (noise, SVM) and 25% (noise, RF), and 80% (reduction, SVM) and 50% (reduction, RF), respectively.     

基于ENVI的遥感图像监督分类方法比较研究

基于监督分类方法在遥感影像分类中的普遍应用,介绍了四种ENVI提供的常用的监督分类方法。对同一TM图像运用这四种方法进行分类,并对分类结果进行了对比,从而分析了这四种方法分类精度之间的差异。     

西北旱区遥感影像分类的支持向量机法

针对较大范围、不同时相、不同气候和地貌类型的遥感影像的土地利用现状分类问题,提出了一种结合标准植被指数和纹理特征的支持向量机法。此方法改进了陕西延安、甘肃嘉峪关和青海果洛的遥感影像分类,有效地解决了最大似然法和BP神经网络法的缺陷造成的分类精度不高的问题。分类结果表明:与最大似然法和BP神经网络法相比,结合标准植被指数和纹理特征的支持向量机法的分类总精度最高(97.75%),Kappa系数为0.9691。该方法可为西北旱区遥感影像解译和土地资源可持续发展战略提供方法支撑。     

Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

A vast portion of Newfoundland and Labrador (NL) is covered by wetland areas. Notably, it is the only province in Atlantic Canada that does not have a wetland inventory system. Wetlands are important areas of research because they play a pivotal role in ecological conservation and impact human activities in the province. Therefore, classifying wetland types and monitoring their changes are crucial tasks recommended for the province. In this study, wetlands in five pilot sites, distributed across NL, were classified using the integration of aerial imagery, Synthetic Aperture Radar, and optical satellite data. First, each study area was segmented using the object-based method, and then various spectral and polarimetric features were evaluated to select the best features for identifying wetland classes using the Random Forest algorithm. The accuracies of the classifications were assessed by the parameters obtained from confusion matrices, and the overall accuracies varied between 81% and 91%. Moreover, the average producer and user accuracies for wetland classes, considering all pilot sites, were 71% and 72%, respectively. Since the proposed methodology demonstrated high accuracies for wetland classification in different study areas with various ecological characteristics, the application of future classifications in other areas of interest is promising.     

Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data,random forest and support vector machines classifiers

The visual progression of sirex (Sirex noctilio) infestation symptoms has been categorized into three distinct infestation phases, namely the green, red and grey stages. The grey stage is the final stage which leads to almost complete defoliation resulting in dead standing trees or snags. Dead standing pine trees however, could also be due to the lightning damage. Hence, the objective of the present study was to distinguish amongst healthy, sirex grey-attacked and lightning-damaged pine trees using AISA Eagle hyperspectral data, random forest (RF) and support vector machines (SVM) classifiers. Our study also presents an opportunity to look at the possibility of separating amongst the previously mentioned pine trees damage classes and other landscape classes on the study area. The results of the present study revealed the robustness of the two machine learning classifiers with an overall accuracy of 74.50% (total disagreement = 26%) for RF and 73.50% (total disagreement = 27%) for SVM using all the remaining AISA Eagle spectral bands after removing the noisy ones. When the most useful spectral bands as measured by RF were exploited, the overall accuracy was considerably improved; 78% (total disagreement = 22%) for RF and 76.50% (total disagreement = 24%) for SVM. There was no significant difference between the performances of the two classifiers as demonstrated by the results of McNemar’s test (chi-squared; χ² = 0.14, and 0.03 when all the remaining ASIA Eagle wavebands, after removing the noisy ones and the most important wavebands were used, respectively). This study concludes that AISA Eagle data classified using RF and SVM algorithms provide relatively accurate information that is important to the forest industry for making informed decision regarding pine plantations health protocols.