Abstract

In this paper, a deep convolutional neural network (CNN) is developed to classify the Unmanned Aerial Vehicle (UAV) derived multispectral imagery and normalized digital surface model (DSM) data in urban areas. For this purpose, a multi-input deep CNN (MIDCNN) architecture is designed using 11 parallel CNNs; 10 deep CNNs to extract the features from all possible triple combinations of spectral bands as well as one deep CNN dedicated to the normalized DSM data. The proposed method is compared with the traditional single-input (SI) and double-input (DI) deep CNN designations and random forest (RF) classifier, and evaluated using two independent test datasets. The results indicate that increasing the CNN layers parallelly augmented the classifier’s generalization and reduced overfitting risk. The overall accuracy and kappa value of the proposed method are 95% and 0.93, respectively, for the first test dataset, and 96% and 0.94, respectively, for the second test data set.

摘要

在本文中，开发了一种深度卷积神经网络 (CNN) 来对无人机 (UAV) 派生的多光谱图像和城市地区的归一化数字表面模型 (DSM) 数据进行分类。为此，使用 11 个并行 CNN 设计了多输入深度 CNN (MIDCNN) 架构；10 个深度 CNN 从光谱带的所有可能的三重组合以及一个专用于归一化 DSM 数据的深度 CNN 中提取特征。将所提出的方法与传统的单输入 (SI) 和双输入 (DI) 深度 CNN 名称和随机森林 (RF) 分类器进行比较，并使用两个独立的测试数据集进行评估。结果表明，增加 CNN 层并行增强了分类器的泛化能力并降低了过拟合风险。