Automated 3D recovery from very high resolution multi-view satellite images

TL;DR

This paper introduces an automated pipeline for processing multi-view satellite images to produce high-quality 3D surface models, utilizing novel fusion and ranking techniques to improve accuracy and detail.

Contribution

A new automated method that fuses multiple stereo-derived depth maps and ranks image pairs based on learned configurations, enhancing 3D reconstruction quality from satellite imagery.

Findings

Achieved an RMSE accuracy improvement of 0.36 meters.

Demonstrated superior results with adaptive median filtering.

Provided detailed analysis of the pipeline's effectiveness.

Abstract

This paper presents an automated pipeline for processing multi-view satellite images to 3D digital surface models (DSM). The proposed pipeline performs automated geo-referencing and generates high-quality densely matched point clouds. In particular, a novel approach is developed that fuses multiple depth maps derived by stereo matching to generate high-quality 3D maps. By learning critical configurations of stereo pairs from sample LiDAR data, we rank the image pairs based on the proximity of the results to the sample data. Multiple depth maps derived from individual image pairs are fused with an adaptive 3D median filter that considers the image spectral similarities. We demonstrate that the proposed adaptive median filter generally delivers better results in general as compared to normal median filter, and achieved an accuracy of improvement of 0.36 meters RMSE in the best case. Results and analysis are introduced in detail.