OGAIS: OpenGL-Driven GPU Acceleration Methodology for 3D Hyperspectral Image Simulation
Hyperspectral remote sensing, which can acquire data in both spectral and spatial dimensions, has been widely applied in various fields. However, the available data are limited by factors such as revisit time, imaging width, and weather conditions. Three-dimensional (3D) hyperspectral simulation bas...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/11/1841 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Hyperspectral remote sensing, which can acquire data in both spectral and spatial dimensions, has been widely applied in various fields. However, the available data are limited by factors such as revisit time, imaging width, and weather conditions. Three-dimensional (3D) hyperspectral simulation based on ray tracing can overcome these limitations by enabling physics-based modeling of arbitrary imaging geometries, solar conditions, and atmospheric effects. This type of simulation offers advantages in acquiring multi-angle and multi-condition quantitative results. However, the 3D hyperspectral simulation requires substantial computational resources. With the development of hardware, a graphics processing unit (GPU) offers a potential way to accelerate it. This paper proposes a 3D hyperspectral simulation model based on GPU-accelerated ray tracing, which is realized by modifying and using a common graphics API (OpenGL). Through experiments, we demonstrate that this model enables 600-band hyperspectral simulation with a computational time of just 2.4 times that of RGB simulation. Furthermore, we analyzed the balance between calculation efficiency and accuracy, and carried out a correlation analysis between ray count and accuracy. Additionally, we verified the accuracy of this model by using UAV-based data. The results demonstrate over 90% spectral curve similarity between simulated and UAV-acquired images. Finally, based on this model, we conducted additional simulation experiments under different environmental variables and observation conditions to analyze the model’s ability to characterize different situations. The results show that the model effectively captures the effects of environmental variables and observation conditions on the hyperspectral characteristics of vehicles. |
|---|---|
| ISSN: | 2072-4292 |