Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery

TL;DR

This paper introduces an automated system for monitoring stockpile volumes using multi-view stereo from SkySat imagery, improving 3D model consistency through RPC refinement for accurate volume measurement.

Contribution

It presents a novel RPC refinement method to enhance 3D model accuracy from satellite imagery, enabling precise volume monitoring of stockpiles.

Findings

RPC refinement improves DSM consistency

Enhanced volume measurement accuracy

Validated system with real coal stockpile data

Abstract

This paper proposes a system for automatic surface volume monitoring from time series of SkySat pushframe imagery. A specific challenge of building and comparing large 3D models from SkySat data is to correct inconsistencies between the camera models associated to the multiple views that are necessary to cover the area at a given time, where these camera models are represented as Rational Polynomial Cameras (RPCs). We address the problem by proposing a date-wise RPC refinement, able to handle dynamic areas covered by sets of partially overlapping views. The cameras are refined by means of a rotation that compensates for errors due to inaccurate knowledge of the satellite attitude. The refined RPCs are then used to reconstruct multiple consistent Digital Surface Models (DSMs) from different stereo pairs at each date. RPC refinement strengthens the consistency between the DSMs of each date, which is extremely beneficial to accurately measure volumes in the 3D surface models. The system is tested in a real case scenario, to monitor large coal stockpiles. Our volume estimates are validated with measurements collected on site in the same period of time.