Application of Feature Optimization and Convolutional Neural Network in Crop Classification

Fine-scale crop classification has always been a hot topic in the field of agricultural remote sensing, which is of great significance for crop yield estimation and planting structure supervision. The emergence of deep learning provides a new way to improve the accuracy of crop classification. Recently, the Convolutional Neural Network (CNN), a representative algorithm of deep learning, shows obvious advantages in processing high-dimensional remote sensing data. However, the application of CNN in crop classification based on multispectral data is still rare, and the classification accuracy dependent on the different feature information of crops is hard to evaluate. In this paper, a crop classification method based on feature selection and CNN for multispectral remote sensing data is proposed to improve fine crop classification. This study used Sentinel-2 remote sensing images as data source. Based on the reflectance of 13 multispectral bands and 10 vegetation indices including normalized difference vegetation index, ratio vegetation index, enhanced vegetation index, etc., the Relief F algorithm was used to rank the contribution of multidimensional features. According to the rank of feature contribution, the features with high contribution were selected and optimized by group training to obtain the best feature collection. Therefore, a CNN-based classification method based on feature selection was designed. Based on this, we classified and mapped the main crops including rice, corn, and peanut in Yuanyang County, Henan Province, with an overall classification accuracy of 96.39%. Meanwhile, the support vector machine and simple CNN were also used to classify the main crops in the research area for comparison. We found that the CNN-based classification method based on the optimal feature collection had the highest classification accuracy, followed by simple CNN, and the support vector machine had the worst performance. The main conclusions of this research are as follows: (1) The Relief F algorithm was effective to sort the contribution of different features. In total, we obtained 24 optimal feature subsets, with a training accuracy of 99.89%; (2) The CNN-based classification method using the optimal feature collection can extract the high-precision difference in features to the greatest extent and realize the fine-scale classification of crops. Compared with simple CNN and support vector machine, the CNN method based on the optimal feature collection has obvious advantages. © 2021, Science Press. All right reserved.