Object-oriented crops classification for remote sensing images based on convolutional neural network

Deep learning technology such as convolutional neural networks (CNN) has achieved outstanding results in the field of crops classification for remote sensing images. The way of land cover or crop types remote sensing classification using CNN is mainly pixel-based classification which is often affected by the phenomenon of "salt and pepper". In order to reduce this effect, an object-oriented crops classification method based on CNN is proposed in this paper. By combining image segmentation technology and CNN model, we use this method to obtain the results of crops classification from Sentinel-2A multi-spectral remote sensing images in Yuanyang County, Henan Province, China. The experiment show that, compared with the pixel level classification based on CNN which only consider the spectral and temporal characteristics of the crops, the method we proposed comprehensively utilizes more detailed information such as spectral feature, texture feature, spatial relationship, and color space. Thus, it gains a better discriminability for some specific crop and achieves higher classification accuracy.