Abstract Unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral imagery with a high spatial resolution (which we refer to here as H2 imagery). As a result of the low operating cost, high flexibility, and the ability to achieve real-time data acquisition, UAV-borne hyperspectral systems have become an important data source for remote sensing based agricultural monitoring. However, precise crop classification based on UAV-borne H2 imagery is a challenging task when faced with a number of different crop classes. The traditional hyperspectral classification methods, such as the spectral-based and object-oriented classification methods, have difficulty in classifying H2 imagery, faced with the problems of salt-and-pepper (SP) noise and scale selection. In this article, the deep convolutional neural network with a conditional random field classifier (CNNCRF) framework is proposed for precise crop classification with UAV-borne H2 imagery. In the proposed method, a deep convolutional neural network (CNN) is designed to extract and fuse in-depth spectral and local spatial features, and the conditional random field (CRF) model further incorporates the spatial-contextual information to improve the problem of holes and isolated regions in the classification map. Meanwhile, virtual sample augmentation based on the hyperspectral imaging mechanism is used to lessen the issue of the limited labeled samples. To validate the results, a new dataset—the Wuhan UAV-borne hyperspectral image (WHU-Hi) dataset—has been built for precise crop classification. The experimental results obtained using the WHU-Hi dataset confirm the accuracy and visualization performance of the proposed CNNCRF classification method, which outperforms the previous methods. In addition, the WHU-Hi dataset could serve as a benchmark dataset for hyperspectral image classification studies.

中文翻译：

---

WHU-Hi：基于深度卷积神经网络和 CRF 的具有高空间分辨率 (H2) 基准数据集和分类器的无人机载超光谱，用于精确作物识别

摘要 无人机 (UAV) 承载的高光谱系统可以获取具有高空间分辨率的高光谱图像（我们在这里称为 H2 图像）。由于运行成本低、灵活性高、能够实现实时数据采集，无人机高光谱系统已成为基于遥感的农业监测的重要数据源。然而，当面对许多不同的作物类别时，基于无人机载 H2 图像的精确作物分类是一项具有挑战性的任务。传统的高光谱分类方法，如基于光谱和面向对象的分类方法，难以对 H2 影像进行分类，面临着椒盐 (SP) 噪声和尺度选择问题。在本文中，提出了具有条件随机场分类器 (CNNCRF) 框架的深度卷积神经网络，用于使用无人机携带的 H2 图像进行精确的作物分类。在所提出的方法中，设计了一个深度卷积神经网络 (CNN) 来提取和融合深度光谱和局部空间特征，条件随机场 (CRF) 模型进一步结合了空间上下文信息以改善空洞问题和分类图中的孤立区域。同时，基于高光谱成像机制的虚拟样本增强被用来减少有限标记样本的问题。为了验证结果，我们构建了一个新的数据集——武汉无人机载高光谱图像 (WHU-Hi) 数据集，用于精确的作物分类。使用 WHU-Hi 数据集获得的实验结果证实了所提出的 CNNCRF 分类方法的准确性和可视化性能，优于以前的方法。此外，WHU-Hi 数据集可以作为高光谱图像分类研究的基准数据集。