A bare-earth extraction algorithm based on graph cut segmentation for electro-optically derived point clouds

Eric J. Hardin

doi:10.1117/12.2576445

12 April 2021 A bare-earth extraction algorithm based on graph cut segmentation for electro-optically derived point clouds

Eric J. Hardin

Author Affiliations +

Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290K (2021) https://doi.org/10.1117/12.2576445
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This paper describes a bare-earth algorithm based on Markov Random Field image segmentation. Many bare earth algorithms exist that were developed for LiDAR. However, a new algorithm was needed to extract bare-earth from point clouds produced by stereo-matching multi-view satellite imagery (called electro-optical (EO) point clouds). EO point clouds have characteristics that pose challenges distinct from LiDAR such as substantially greater noise levels and missing data due to object occlusion. Despite these challenges, the algorithm accurately extracts bare-earth from EO point clouds. Additionally, the algorithm is robust to sensor type, which was demonstrated by applying the algorithm to LiDAR surveys collected with different sensors. The algorithm is shown to be robust to different levels of urban development and terrain variability and achieves a 94% accuracy on average when compared to manually classified point clouds.

Conference Presentation

Citation Download Citation

Eric J. Hardin "A bare-earth extraction algorithm based on graph cut segmentation for electro-optically derived point clouds", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290K (12 April 2021); https://doi.org/10.1117/12.2576445

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