杨钊霞^*， 杨钊霞

（  中南大学地球科学与信息物理学院，长沙 410083；  ）

摘要： 作为获取遥感信息的重要手段，遥感影像分类一直是遥感领域内重要的研究内容，可以为遥感影像的其他应用提供基础的分析数据。 传统的遥感影像分类方法主要是依据影像中目标（对象或像元）的光谱特性确定其所属类别。然而由于同物异谱、异物同谱现象的存在，仅用光谱特征进行分类，分类精度较低，而空间信息的加入则可以提高分类精度。另外，由于从高光谱影像中可以提取地物的平面信息，从激光雷达数据中可以提取地物的高度等空间立体信息，两者正好可以形成优势互补，因此，为了进一步提高分类精度，应将光谱数据与雷达数据联合起来进行分类。 基于以上分析，本文提出了一种空-谱特征与高程特征相结合的多源数据联合分类方法。首先，使用最小噪声分离变换方法对原始高光谱影像进行降维处理，在此基础上，对主成分图进行空-谱特征的提取。其次，对激光点云构成的影像进行滤波并进行反距离加权内插处理以获取地物的高程信息。然后，将不同来源的特征组合起来，以得到组合特征。最后，使用支持向量机分类器对高光谱影像进行分类。实验证明，本文方法提取的特征可以有效表示影像，实现更高精度的地物分类。
关键词： 高光谱影像；激光雷达；空-谱特征；高程特征；地物分类

TAO Chao^1,*， YANG Zhaoxia^2,

（  1、School?of?Geosciences?and?Info-Physics,?Central?South?University,?Chang sha, 410083；       2、School?of?Geosciences?and?Info-Physics,?Central?South?University, Chang sha, 410083；  ）

Abstract： As an important means of remote sensing image information extraction, remote sensing image classification has always been important in the field of remote sensing research, providing basis analysis of the data for other applications of remote sensing images. Traditional remote sensing image classification methods determine the category of image target (object or pixel) based on its spectral characteristics . However, due to the presence of same object with different spectrums and different objects with same spectrum, if only using spectral characteristics to distinguish targets ,the classification accuracy is low, and spatial information can be added to improve the classification accuracy. In addition, we can extract the plane information of the detected object from hyperspectral image, and extract the height information from laser radar data. Therefore, in order to further improve the classification accuracy, we can combine the spectral data with the radar data. According to the above analysis, we propose a novel approach of multi-source data classification based on the combination of spatial-spectral feature and elevation feature. First, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components (PCs) into a vector representation after using minimum noise fraction to reduce dimensionality of the original hyperspectral image. Then, we get the feature of elevation information through processing the lidar image such as filtering and inverse distance weighting interpolation. Secondly, we combine features obtained from different data sources. Finally, we embed the resulting sparse feature coding into the support vector machine (SVM) for hyperspectral image classification. Experiments show our approach can extract the effective feature to represent the image, and achieve higher classification accuracy of ground objects.
Keywords： hyperspectral image; lidar; spatial-spectral feature; elevation feature; land-covers classification

PDF全文下载：     初稿（41）

【收录情况】