Crops Classification from Sentinel-2A Multi-spectral Remote Sensing Images Based on Convolutional Neural Networks

Abstract

Deep learning technology such as convolutional neural networks (CNN) can extract the distinguishable and representative features of different land cover from remote sensing images in a hierarchical way to classify. However, in the field of agriculture, there are few application of crops classification from multi-spectral remote sensing images based on deep learning. In this context, we compared the classification methods of CNN and support vector machines (SVM) in extracting the spatial distribution of crops planting area from Sentineal-2A multi-spectral remote sensing images in Yuanyang county, China. For the region of study, both methods obtained reasonable spatial distribution of different crops, the verification results show that the overall accuracy of CNN is 95.6% which is superior to SVM.