Fusion of complementary information from multisensor data is of great importance for identifying the land covers. However, integration of multisource information is a challenging task. A framework is developed to integrate hyperspectral and LiDAR data for land cover classification. In the proposed method, sparse stacked autoencoders are used to represent the spectral and spatial information in a compact form. The spatial information is extracted both from hyperspectral and LiDAR data using morphological operators. The encoded spectral and spatial features are combined with elevation information to form a joint feature vector. The joint features are fed to a convolutional neural network (CNN) classifier to classify the land covers. The CNN classifier is a hybrid three- and two-dimensional (3D)–(2D) model having three 3D convolutional layers and one 2D convolutional layer. The experiments are carried out on two datasets Houston and Samford to evaluate the performance of the proposed method. The results have demonstrated the effectiveness of the method with global κ = 0.9285 and global naive accuracy (OA) of 93.44% for Houston data. For Samford data, it achieves κ = 0.9811 and OA = 98.93 % .

中文翻译：

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使用稀疏堆叠自动编码器融合高光谱和 LiDAR 数据，通过 3D-2D 卷积神经网络进行土地覆盖分类

融合来自多传感器数据的互补信息对于识别土地覆盖非常重要。然而，多源信息的集成是一项具有挑战性的任务。开发了一个框架来整合用于土地覆盖分类的高光谱和激光雷达数据。在所提出的方法中，稀疏堆叠的自动编码器用于以紧凑的形式表示光谱和空间信息。空间信息是使用形态算子从高光谱和激光雷达数据中提取的。编码后的光谱和空间特征与高程信息相结合，形成一个联合特征向量。联合特征被馈送到卷积神经网络 (CNN) 分类器以对土地覆盖进行分类。CNN 分类器是一个混合三维和二维 (3D)-(2D) 模型，具有三个 3D 卷积层和一个 2D 卷积层。实验在休斯顿和桑福德两个数据集上进行，以评估所提出方法的性能。结果证明了该方法的有效性，全局 κ = 0.9285 和 93.44% 的全局朴素准确率 (OA) 对于休斯顿数据。对于 Samford 数据，它实现了 κ = 0.9811 和 OA = 98.93 % 。