We explore the application of single image super-resolution technique to satellite image and its effect on object detection performance. This technique uses a deep convolutional neural network to learn transformations between different zoom levels of image pyramids, also referred to as Resolution Set (Rset). The network can learn the transformations from the 2:1 RSet at a Ground Sample Distance (GSD) of 60cm to the full resolution image at a GSD of 30cm by minimizing the differences between ground-truth full resolution and the derived 2x zoom. After training, the learned transformation is applied to the 1:1 full resolution image transforming the pixels to 2x resolution. The learned transformations has intelligence built in and can infer higher resolution images. We find super-resolution images significantly improve object detection accuracy, improve manual feature extraction accuracy, and also benefit imagery analysis workflows and derived products which use satellite images.
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