改进相似性测度的高光谱影像FCM聚类分析

高光谱遥感将反映目标辐射属性的光谱信息与反映目标空间几何关系的图像信息有机地结合在一起,能够实现地面目标的精细分类识别。FCM是一种有效的聚类算法,但存在相似性测度模型单一、分类精度的提高受到限制等问题。文中结合高光谱影像的技术特点,综合考虑光谱曲线的形状、地物辐射亮度及其权重,提出可以更好描述光谱向量之间的相似性的距离测度,并将其引入到FCM聚类模型中。聚类分析试验结果表明：通过改进和优化相似性测度的FCM,可以显著提高高光谱影像聚类精度。     

基于新型相似性测度的高光谱影像地物检测

提出了一种新型光谱相似性测度及其参数的自适应选择方法,并且将其应用到了高光谱影像地物检测中。由于这种相似性测度基于光谱角度余弦(SAC),因此在理论上对因光照强度变化、阴影和遮挡等引起的同种地物光谱变化的适应性较强。最后利用两幅高光谱影像进行了实验分析,实验结果证明提出的方法不仅能扩大阈值取值区间,而且可提高检测的精度。     

结合邻域SAC-NED测度的高光谱图像分类方法

提出了一种光谱相似性测度用于高光谱图像分类方法。通过将光谱向量进行归一化处理,将计算得到的欧氏距离与光谱角余弦的值域归化到相同区间,得到光谱角余弦与欧氏距离联合测度值(SAC-NED)。在对图像像元进行分类时,以距离加权的方式将邻域像元参与中心像元SAC-NED值的计算,将像元分到SAC-NED值最大的类别。通过与其他5种常用相似性测度方法的实验结果对比表明:该算法能够提升高光谱图像分类的准确性和稳定性。     

结合空间信息的EDCC测度光谱匹配分类方法

光谱匹配分类方法以光谱相似性测度为分类准则,一种相似性测度只对应于光谱曲线的一种特征,用于光谱匹配分类效果并不好;组合不同类型的相似性测度能够有效改善分类效果,但光谱匹配分类往往忽略了相邻像元间的相关性。为了更好地利用空间信息,提高光谱匹配分类精度,首先组合欧氏距离测度和相关系数测度,得到欧氏距离-相关系数测度;其次通过加入空间乘子,得到结合空间信息的欧氏距离-相关系数测度,从而在光谱匹配分类中增加了空间信息约束。采用两组高光谱影像进行实验验证,结果表明,相比于单一相似性测度及组合相似性测度,结合空间信息的欧氏距离-相关系数测度用于光谱匹配分类能够有效改善分类精度。     

联合局部二值模式的高光谱影像空-谱分类方法

为充分利用高光谱影像"图谱合一"的特性,提出了一种联合局部二值模式的高光谱影像空-谱分类方法。该方法通过局部二值模式从降维影像中提取空间纹理特征,以线性加权求和核为多核组合方式,与原始光谱特征结合构造混合核极限学习机模型,实现影像的地物分类。为了验证该方法的有效性,利用Indiana和Pavia U两组高光谱影像数据进行实验,总体分类精度分别达到99.23%和94.95%。结果表明该方法分类效果优于纯光谱分类、纯局部二值模式空间分类、GLCM空-谱分类以及3Gabor空-谱分类方法,有效地改善了高光谱影像分类结果,获得更加平滑的分类结果图。     

一种多特征转换的高光谱影像自适应分类方法

:光谱相似性测度用来衡量像元光谱的相似程度,是高光谱影像光谱匹配分类的重要工具之一,一般通过设置阈值判断像元光谱和参考光谱是否相似来进行分类。在此基础上,本文提出了一种多特征转换的高光谱影像自适应分类方法,实现了各种光谱相似性特征和分类器相结合的一种自适应分类。实验结果表明,本文提出的方法相比于传统的SVM方法,分类的总体精度更高,还可以避免部分传统光谱匹配分类方法中需要专家经验确定分类阈值的复杂过程。     

结合像元空间邻域信息的高光谱影像分类

针对传统的光谱角匹配分类算法仅考虑光谱信息,导致混合像元易出现错分和分类结果中出现"麻点"等问题,该文考虑地物连续性这一特点,提出了一种结合像元空间邻域信息对光谱角进行修正的光谱角匹配分类算法。该方法不仅保留了传统光谱角度匹配算法不受增益因素影响和减弱地形对照度影响等优点,并且减小了混合像元被错分的概率。基于ROSIS获取的Pavia大学校园的高光谱影像分类结果表明:加入像元空间邻域信息后的光谱角匹配算法在保证分类精度的同时,有效地减弱了分类结果中的"麻点"现象,验证了该文方法的可行性、有效性。     

利用ICA正交子空间投影加权的高光谱影像目标探测算法

提出了一种利用独立成分分析（ICA）正交子空间投影加权的高光谱影像目标探测方法。该方法从影像像元集合的独立成分入手,通过一种光谱相似性测度加权,赋予每个像素合适的权值,从而有效地解决从原始影像中无法正确提取背景数据而造成的虚警概率高的问题。实验结果表明,相比于经典的CEM方法,在相同的探测概率下,该方法能降低1.97%的虚警概率;与相关目标探测算法相比,所提出的算法具有较好的目标探测效果。     

空-谱融合的条件随机场高光谱影像分类方法

高光谱遥感数据具有光谱信息丰富、图谱合一的特点,目前已经广泛地应用在对地观测中。传统的高光谱分类模型大多过分依赖影像光谱信息,没有充分利用空间特征信息,这使得分类精度还有很大的提升空间。条件随机场是一种概率模型,能够较好地融合空间上下文信息,在高光谱影像分类中已经得到越来越多的关注,但大部分条件随机场模型存在超平滑的现象,会导致影像细节丢失。针对该问题,本文提出了一种优化融合影像空-谱信息的高分辨率/高光谱影像分类方法,该方法将影像的纹理信息与原始光谱信息进行融合,利用SVM分类器对其进行预分类,并将各类概率定义为一元势函数,以融合空间特征信息;然后将空间平滑项和局部类别标签成本项加入二元势函数中,以考虑空间背景信息,并保留各类别中的详细信息。最后,通过两组的高分辨率/高光谱影像数据进行试验。结果表明,与SVM算法、传统的条件随机场方法和面向对象的分类方法相比,本文提出的算法在整体分类精度上分别提高了10%、9%和8%以上,同时在保持地物边缘完整性、避免“同谱异物”与“同物异谱”的现象方面有较明显的优势。     

高光谱影像空-谱协同嵌入的地物分类算法

针对传统高光谱影像地物分类算法大多仅考虑光谱信息而忽略空间邻近像元间相关性的问题,提出了一种空-谱协同嵌入(SSCE)降维算法和空-谱协同最近邻(SSCNN)分类器。首先,定义一种空-谱协同距离,并将其应用于近邻选取和低维嵌入;然后,构建空-谱近邻关系图来保持数据中的流形结构,并在权值设置中增大空间近邻点的权重以增强数据间的聚集性,提取鉴别特征;最后使用SSCNN分类器对降维后的数据进行分类。利用PaviaU和Salinas高光谱数据集进行试验验证,结果表明,与传统的光谱分类算法相比,该算法能有效提高高光谱影像的地物分类精度。     

高光谱遥感光谱相似性度量算法与若干新方法研究

提出了一种新的光谱相似性度量算法分类体系。在归纳算法的基础上,根据不同的度量原理与实现策略,结合应用需求,提出了基于光谱多边形的测度、四值编码、十进制编码、树状变换测度及基于小波变换的测度等新方法,这些方法能够应用于分类、检索等的相似性度量中。     

Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image

The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r?=?0.982 and computed SID equivalent r?=?0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure.     

Automatic Pseudo-invariant Feature Extraction for the Relative Radiometric Normalization of Hyperion Hyperspectral Images

A new relative radiometric normalization approach is presented based on the spectral profile shape of hyperspectral data. We calculate the spectral similarity value of pixels at the same location using spectral angle mapping. The cumulative moving average and its differential values are used to determine the appropriate number of pseudo-invariant features automatically. Band-by-band linear regression of the pseudo-invariant features is used to refine the radiometric normalization results iteratively. We tested the algorithm using six Hyperion data subset images. The proposed method yielded stable results with similar or better performance than other methods for all test sites, when assessed by visual inspection and quantitative analysis.     

利用相似性度量的不同比例尺地图数据网状要素匹配算法

提出了一种基于相似性度量的不同比例尺地图数据网状要素匹配算法。首先进行结点、弧段的粗匹配,然后利用结点-弧段拓扑关系的相似性和离散Fréchet距离进行精确匹配,匹配过程将几何、语义、拓扑、结点和弧段匹配有效结合起来,最后以可视化方式将不同匹配结果进行显示,以便人机交互。实验表明,该算法可有效地匹配各种复杂情况下的同名道路,并提高匹配的正确率和速度。     

A Geostatistically Based Preprocessing Algorithm for Hyperspectral Data Analysis

We present a preprocessing algorithm for hyperspectral remote sensing datasets. The algorithm is based on a geostatistical method and should be helpful when a spatial relationship is detected in a dataset. One significant advantage of hyperspectral remote sensing using spectral profiles is the ability to compare an unknown pixel's profile with endmembers that have already been identified by a variety of methods (e.g., laboratory experiments with high-precision spectrometers), with the final goal of determining the unknown pixel. The conditions under which the airborne or spaceborne hyperspectral data are collected, however, differ from those that prevail in the laboratory or field. Therefore, a dataset should be preprocessed so as to eliminate or considerably reduce these differences; the algorithm presented here could be used for that purpose. The result will not only improve the smoothness of spectral profiles, but it may also offer advantages for geological investigations to study mineral anomalies using hyperspectral data. Concentrations of minerals in rock bodies often have certain patterns and follow trends that can be modeled by computing a semivariogram. The advantages of using such a trend have induced mining engineers to develop innovations in geostatistics. These trends should be taken into account when handling hyperspectral datasets. In all methods presented for boosting spectral profiles, the spatial relationships among pixels' DNs are neglected, but, in the method presented here, this relationship is calculated by geostatistics, and an algorithm is applied to improve spectral profiles. The nugget effect is calculated separately for each channel, and its square root is subtracted from the reflectance of all pixels in that channel. Finally, we examine the effectiveness and validation of the method examined using the AVIRIS dataset from Cuprite, Nevada. The results are satisfactory, as the algorithm yields a better mineral detection process.     

EO-1Hyperion高光谱数据FLAASH模块大气纠正及评价

由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节。文章介绍了EO-1hyperion高光谱数据的特点,以及用FLAASH（Fast Line of Sight Atmospheric Analysis of ...     

一种基于变权重组合的光谱相似性测度

光谱相似性测度是高光谱遥感影像信息提取的关键。在欧氏距离和光谱角余弦的基础之上提出一种变权重组合的光谱相似性测度,即光谱变化权重相似性测度。这种光谱相似性测度可根据不同地物类别自动对欧氏距离和光谱角余弦测度指标配比权重。选用标准光谱库和机载OMIS高光谱影像对SCWM进行测试,并引入误分率和混淆矩阵对分类结果进行评价。结果表明,相对于仅采用一种或两种光谱相似性测度的分类方法,光谱变化权重相似性测度具有更精细的光谱识别能力。     

A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data

With recent technological advances in remote sensing sensors and systems, very high-dimensional hyperspectral data are available for a better discrimination among different complex land-cover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or ‘curse of dimensionality’ in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated. Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifier in these situations, Multiple Classifier Systems (MCS) may have better performance than single classifier. This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. The proposed method uses the band grouping process based on a modified mutual information strategy to split data into few band groups. After the band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, the proposed approach applies SVM on each band group that is produced in a previous step. Finally, Naive Bayes (NB) as a classifier fusion method combines decisions of SVM classifiers. Experimental results on two common hyperspectral data sets show that the proposed method improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods.