WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF

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.