Investigating ground objects widely distributed in geography and large in scale is one of the primary missions for satellite sensors. On the other hand, recognizing objects from images is one of the classic tasks for convolutional neural networks (CNNs), currently the most popular computer vision technique. Data processing, such as data augmentation, channel selection, and image fusion, can be essential when applying CNNs to satellite images. With a case study of recognizing solar panels from satellite images using CNN, the related data processing issues are discussed, and an approach to embed channel fusion methods into CNN is established. As a result, the following findings are concluded from our case study: (1) not all channels in satellite images contribute to specific object recognition, and thus channel selection is necessary in applying CNN on satellite images; (2) fine-tuning the fusion method embedded in CNN improves the model stability; and (3) transfer learning is outperformed by CNN models trained with augmented data for object recognition from satellite images.