Satellite imagery presents a number of challenges for object detection, such as the significant variation in object size (from small cars to airports), and low object resolution. In this work, we focus on recognizing objects taken from the xView Satellite Imagery dataset. The xView dataset introduces its own set of challenges, the most prominent being the imbalance between the 60 classes present. xView also contains considerable label noise as well as both semantic and visual overlap between classes. In this work, we focus on techniques to improve performance on an imbalanced, noisy dataset through data augmentation and balancing. Additionally, we show that a very small convolutional neural network (SAT-CNN) with approximately three million parameters can outperform a deep pre-trained classifier, VGG16 - which is used for many state-of-the-art tasks - with over 138 million parameters.