Crop classification is necessary to extract information about the crop, such as its type, the area in crop that may be used to estimate health, and the yield of the crop. Remote sensing is a technique used to extract earth surface information from satellite and aerial imagery. However, to extract crop parameters accurately requires high spatial and spectral information of the data, which can be achieved through image fusion. We focus on crop classification by selecting the suitable band combination of satellite imagery and unmanned aerial vehicle (UAV) data through image fusion technique. Various fusion techniques are available to produce high spatial and spectral resolution data. Principal component analysis PAN sharpening method is used to fuse Sentinel-2A and UAV data for crop classification, and the best combinations of different bands are assessed based on the image quality metrics and classification accuracy. Random forest classification algorithm is performed to classify the fused images and the classification accuracy is assessed by using the F-score, Kappa coefficient, and overall accuracy. The best accuracy of 93.14% and a kappa value of 0.87 is achieved using random forest classification technique using fused images of red band of UAV imagery with BGRNIR (blue, green, red, and NIR) bands of Sentinel-2A imagery is high compared to other band combinations.