UAV image crop classification based on deep learning with spatial and spectral features

Abstract

Unmanned Aerial Vehicle image analysis technology has become an effective means to classify crops. However, the UAV image classification mostly adopts shallow learning algorithm, with few computing units. These methods have low recognition accuracy and poor generalization ability. Deep learning has powerful function expression and excellent feature learning abilities. The learned features have more essential character for data and have achieved remarkable results in image classification. Therefore, the study proposes a crop classification method based on Unmanned Aerial Vehicle image with deep learning and spatial and spectral feature fusion. The method uses deep convolutional neural network to classify Unmanned Aerial Vehicle images. Simplified AlexNet network structure, which reduces the number of network layers, was used to accelerate the convergence speed of the model while ensuring the accuracy of crop classification in practical applications. Then, the vegetation index and height features of the Unmanned Aerial Vehicle image were extracted. Feature combination and comparative analyses were carried out to find the most effective feature combination and improve the accuracy of crop classification through spatial and spectral feature fusion. In addition, a Sample Expansion Strategy was used to optimize the classification model and further improve the classification result to achieve a perfect performance in the crop classification of drone images. The experimental results showed that the deep learning method can effectively identify crop types in Unmanned Aerial Vehicle images, and the overall classification accuracy is raised from 86.07% to 92.76% when combining spatial and spectral feature fusion with Sample Expansion Strategy.