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

arXiv: https://arxiv.org/abs/1905.07475 · HTML: https://mlsanity.com/paper/1905.07475/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 6 of 6)

- [Image Fusion in Remote Sensing: An Overview and Meta Analysis](https://mlsanity.com/paper/2401.08837/) (2024)
- [A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images](https://mlsanity.com/paper/2210.14031/) (2022)
- [3D Reconstruction through Fusion of Cross-View Images](https://mlsanity.com/paper/2106.14306/) (2021)
- [On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data](https://mlsanity.com/paper/2310.10490/) (2023)
- [Individual Tree Detection and Crown Delineation with 3D Information from Multi-view Satellite Images](https://mlsanity.com/paper/2107.00592/) (2021)
- [Robust 3D Reconstruction from Multi-View Optical Satellite Imagery via Reliability-Aware Height-Evidence Fusion in Gaussian Splatting](https://mlsanity.com/paper/2609.16772/) (2026)

## Abstract (arXiv)

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.