Semantic Stereo for Incidental Satellite Images

By Marc Bosch, Kevin Foster, Gordon A. Christie, Sean Wang, Gregory Hager, Myron Z. Brown. 2018

189 citations recorded by the source corpus.

The increasingly common use of incidental satellite images for stereo reconstruction versus rigidly tasked binocular or trinocular coincident collection is helping to enable timely global-scale 3D mapping; however, reliable stereo correspondence from multi-date image pairs remains very challenging due to seasonal appearance differences and scene change. Promising recent work suggests that semantic scene segmentation can provide a robust regularizing prior for resolving ambiguities in stereo correspondence and reconstruction problems. To enable research for pairwise semantic stereo and multi-view semantic 3D reconstruction with incidental satellite images, we have established a large-scale public dataset including multi-view, multi-band satellite images and ground truth geometric and semantic labels for two large cities. To demonstrate the complementary nature of the stereo and segmentation tasks, we present lightweight public baselines adapted from recent state of the art convolutional neural network models and assess their performance.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering

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In Computer Vision and Pattern Recognition

1. Valerio Marsocci
2. Simone Scardapane
3. Laura Palagi

Citations in the corpus

• Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, background): “As far as the US3D dataset is concerned, again selfsupervision techniques lead to better results on downstream tasks, highlighting how, in this case, CBT performs best of all (CBT 84.56% vs ImageNet 83.32% mIoU).”