Ground 3D Object Reconstruction Based on Multi-View 3D Occupancy Network using Satellite Remote Sensing Image
- Resource Type
- Conference
- Authors
- Chen, Hao; Chen, Wen; Gao, Tong
- Source
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Geoscience and Remote Sensing Symposium IGARSS , 2021 IEEE International. :4826-4829 Jul, 2021
- Subject
- Aerospace
Geoscience
Photonics and Electrooptics
Signal Processing and Analysis
Three-dimensional displays
Satellites
Estimation
Reconstruction algorithms
Feature extraction
Image reconstruction
Remote sensing
Multi-view 3D reconstruction
Ground objects
Satellite remote sensing image
Occupancy network
- Language
- ISSN
- 2153-7003
It is challenging to achieve high accuracy and rich details three-dimensional (3D) reconstruction of ground object using satellite remote sensing image (RSI). A 3D reconstruction method is proposed to address this problem based on multi-view 3D occupancy network. The domain adaptive multi-view image obtained by pretreatment is input into the depth residual network to extract perceptual features at first. The occupancy probability of objects at different coordinates in 3D space is obtained by combining spatial probability network with definition balance strategy. Then, the isosurface is extracted to obtain the 3D mesh of the object, and the reconstruction result is given scale by combining the attitude estimation and the original RSI parameter information. Experiments are carried out on a testing dataset composed of hundreds of object image pairs. The experimental results show that the proposed method for satellite RSI has better accuracy than the typical single-view and multi-view reconstruction methods.