An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery
An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery
复制标题
用于从城市高光谱图像中提取和分类目标的自适应均值漂移分析方法
DOI：
10.1109/tgrs.2008.2002577
复制
发表时间：
2008-12-01
影响因子：
8.2
通讯作者：
Zhang, Liangpei
中科院分区：
文献类型：
--
作者：
Huang, Xin;Zhang, Liangpei
本文提出了一种自适应均值漂移（MS）分析框架，用于城市地区高光谱图像的目标提取和分类。其基本思想是应用MS获得高光谱数据的面向对象表示，然后使用支持向量机来解释特征集。为了有效地将MS应用于高光谱数据，利用特征提取算法——非负矩阵分解来减少高维特征空间。此外，还为 MS 过程提出了两种带宽选择算法。一种基于局部结构，另一种利用可分离性分析。实验在两个高光谱数据集上进行，即 DC 购物中心高光谱数字图像收集实验和普渡大学校园高光谱测绘仪图像。我们将所提出的方法与著名的商业软件 eCognition（基于对象的分析方法）和高光谱数据的有效光谱/空间分类器（即形态剖面的导数）进行评估和比较。实验结果表明，所提出的基于MS的分析系统是鲁棒的，并且明显优于其他方法。
In this paper, an adaptive mean-shift (MS) analysis framework is proposed for object extraction and classification of hyperspectral imagery over urban areas. The basic idea is to apply an MS to obtain an object-oriented representation of hyperspectral data and then use support vector machine to interpret the feature set. In order to employ MS for hyperspectral data effectively, a feature-extraction algorithm, nonnegative matrix factorization, is utilized to reduce the high-dimensional feature space. Furthermore, two bandwidth-selection algorithms are proposed for the MS procedure. One is based on the local structures, and the other exploits separability analysis. Experiments are conducted on two hyperspectral data sets, the DC Mall hyperspectral digital-imagery collection experiment and the Purdue campus hyperspectral mapper images. We evaluate and compare the proposed approach with the well-known commercial software eCognition (object-based analysis approach) and an effective spectral/spatial classifier for hyperspectral data, namely, the derivative of the morphological profile. Experimental results show that the proposed MS-based analysis system is robust and obviously outperforms the other methods.