A novel ensemble classifier of hyperspectral and LiDAR data using morphological features
A novel ensemble classifier of hyperspectral and LiDAR data using morphological features
复制标题
DOI：
10.1109/icassp.2017.7953345
复制
发表时间：
2017-03
期刊：
影响因子：
--
通讯作者：
J. Xia;N. Yokoya;A. Iwasaki
中科院分区：
文献类型：
--
作者：
J. Xia;N. Yokoya;A. Iwasaki
由于不同遥感传感器的优点和局限性，融合高光谱和光探测与测距(LiDAR)等多个传感器的特征是进行土地覆盖制图的有效方法。在本文中，我们提出了一种新的集成分类器来融合高光谱和激光雷达数据集进行分类。首先，使用形态特征对原始超光谱(HS)图像和LiDAR数据的前几个主成分(PC)的空间和高程信息进行建模。其次，我们将不同类型的特征(即光谱波段、高光谱形态特征和激光雷达特征)分成几个不相交的子集，并对每个子集应用数据转换方法。重点研究了主成分分析(PCA)、线性保持投影(LPP)和无监督图融合(UGF)三种数据变换方法。第三，在每个子集中提取的特征通过随机森林(RF)分类器进行连接以进行分类。在HS和LiDAR联合注册数据上的实验结果验证了集成分类器的有效性和潜力。
Due to the benefits and limitation of different remote sensing sensors, fusion of the features from multiple sensors, such as hyperspectral and light detection and ranging (LiDAR) is an effective method for land cover mapping. In this paper, we propose a novel ensemble classifier to fuse hyperspectral and LiDAR datasets for classification. First, morphological features are used to model spatial and elevation information from the first few principal components (PCs) of the original hyperspetcral (HS) image and LiDAR data. Second, we split different kinds of features (i.e., spectral bands, morphological features of hyperspectral and LiDAR), into several disjoint subsets and apply the data transformation method to each subset. In particular, three data transformation methods, including principal component analysis (PCA), linearity preserving projection (LPP) and unsupervised graph fusion (UGF) are considered. Third, the features extracted in each subset are concatenated to classify by a random forest (RF) classifier. Experimental results on a co-registered HS and LiDAR data provide the effectiveness and potentialities of the proposed ensemble classifier.