Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

Accurate and timely crop mapping is essential for agricultural applications, and deep-learning methods have been applied on a range of remotely sensed data sources to classify crops. In this article, we develop a novel crop classification method based on spatiotemporal deep-learning fusion technology. However, for crop mapping, the selection and labeling of training samples is expensive and time consuming. Therefore, we propose a fully automated training-sample-selection method. First, we design the method according to image processing algorithms and the concept of a sliding window. Second, we develop the Geo-3D convolutional neural network (CNN) and Geo-Conv1D for crop classification using time-series Sentinel-2 imagery. Specifically, we integrate geographic information of crops into the structure of deep-learning networks. Finally, we apply an active learning strategy to integrate the classification advantages of Geo-3D CNN and Geo-Conv1D. Experiments conducted in Northeast China show that the proposed sampling method can reliably provide and label a large number of samples and achieve satisfactory results for different deep-learning networks. Based on the automatic selection and labeling of training samples, the crop classification method based on spatiotemporal deep-learning fusion technology can achieve the highest overall accuracy (OA) with approximately 92.50% as compared with Geo-Conv1D (91.89%) and Geo-3D CNN (91.27%) in the three study areas, indicating that the proposed method is effective and efficient in multi-temporal crop classification.