Deducing 15 cm detail from 30 cm satellite images with deep learning

Bingcai Zhang; Yen Luu; Kalyan Vaidyanathan; Fidel Paderes; Reuben Settergren; Kurt de Venecia

doi:10.1117/12.2622690

6 June 2022 Deducing 15 cm detail from 30 cm satellite images with deep learning

Bingcai Zhang, Yen Luu, Kalyan Vaidyanathan, Fidel Paderes, Reuben Settergren, Kurt de Venecia

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 1211310 (2022) https://doi.org/10.1117/12.2622690
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Bingcai Zhang, Yen Luu, Kalyan Vaidyanathan, Fidel Paderes, Reuben Settergren, and Kurt de Venecia "Deducing 15 cm detail from 30 cm satellite images with deep learning", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 1211310 (6 June 2022); https://doi.org/10.1117/12.2622690

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