An effective approach for land-cover classification from airborne lidar fused with co-registered data
An effective approach for land-cover classification from airborne lidar fused with co-registered data
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DOI：
10.1080/01431161.2012.676746
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发表时间：
2012-09
影响因子：
3.4
通讯作者：
Yang Cao;Hong Wei;Huijie Zhao;Na Li
中科院分区：
文献类型：
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作者：
Yang Cao;Hong Wei;Huijie Zhao;Na Li
机载激光雷达提供了地球上物体的精确高度信息，在许多应用中被认为是一种可靠和精确的测量工具。特别是，激光雷达数据为城市土地覆盖分类提供了重要和重要的特征，这是城市土地利用研究的一项重要任务。在这篇文章中，我们提出了一种有效的方法，激光雷达数据融合其共同注册的图像（即航空彩色图像包含红色，绿色和蓝色（RGB）波段和近红外（NIR）图像）和其他衍生功能有效地用于准确的城市土地覆盖分类。所提出的方法开始于一个初始分类进行的证据与一个专门设计的基本概率分配函数的Dempster-Shafer理论。它输出两个结果，即初始分类和伪训练样本，这是根据组合概率质量自动选择。其次，采用基于支持向量机（SVM）的概率估计器，从伪训练样本中计算每个像素的类条件概率（CCP）。最后，建立了一个马尔可夫随机场（MRF）模型，联合收割机的空间上下文信息结合到分类。在这个阶段中，利用初始分类结果和CCP。针对最大后验概率（MAP）-MRF框架，提出了一种有效的信念传播（EBP）算法，用于搜索全局最小能量解。激光雷达及其由Toposys Falcon II获得的共同注册数据用于性能测试。实验结果表明，高度数据和光学图像的融合特别适合于城市土地覆盖分类。该方法不需要训练样本，计算量相对较小。平均分类准确率为93.63%。
Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.