In deep learning, data augmentation is important to increase the amount of training images to obtain higher classification accuracies. Most data-augmentation methods adopt the use of the following techniques: cropping, mirroring, color casting, scaling and rotation for creating additional training images. In this paper, we propose a novel data-augmentation method that transforms an image into a new image containing multiple rotated copies of the original image in the operational classification stage. The proposed method creates a grid of n×n cells, in which each cell contains a different randomly rotated image and introduces a natural background in the newly created image. This algorithm is used for creating new training and testing images, and enhances the amount of information in an image. For the experiments, we created a novel dataset with aerial images of cows and natural scene backgrounds using an unmanned aerial vehicle, resulting in a binary classification problem. To classify the images, we used a convolutional neural network (CNN) architecture and compared two loss functions (Hinge loss and cross-entropy loss). Additionally, we compare the CNN to classical feature-based techniques combined with a k-nearest neighbor classifier or a support vector machine. The results show that the pre-trained CNN with our proposed data-augmentation technique yields significantly higher accuracies than all other approaches.