Crop classification based on G-CNN using multi-scale remote sensing images

Crop classification is important for monitoring crop growth and ensuring national food security. Crop plots have a complex spatial planting structure with a certain degree of fragmentation, which leads to mixing of different crops with unclear borders, in turn affecting the ability of the classification model to recognize and distinguish between crops. To address the above problems, this paper proposes a crop classification method based on multi-source optical remote sensing data, which can extract crop information more accurately by taking advantage of the multi-spectral features and different spatial resolutions of different optical data. Firstly, spectral bands and vegetation indexes are extracted from Sentinel-2B and Jilin Gaofen (JLGF02B). Then, a Gemel Convolutional Neural Network (G-CNN) is constructed to fully mine and integrate the feature information at different scales for crop classification. Finally, the method is compared with classic deep learning algorithms. The result shows that the proposed G-CNN algorithm gets the best results with an overall accuracy OA of 96.5% and a Kappa coefficient of 94.7%. The G-CNN model provides a new idea and method for crop classification using multi-scale optical data.