研究目的
To study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area.
研究成果
The study demonstrated the potential of UAV-based remote sensing with FPI-based hyperspectral cameras for accurate 3-D surface modeling and surface moisture estimation in peat production areas. The best moisture estimation accuracy was achieved using the reflectance difference of SWIR and VNIR bands, indicating the technology's promise for improving efficiency and environmental safety in peat production.
研究不足
The SWIR camera was a new prototype with incomplete radiometric calibration, leading to the need for empirical corrections. The geometric performance of the SWIR data set was worse due to poorer block structure and lower image quality. The study area was limited to a flat peat production area, and the methods may require adaptation for more complex environments.
1：Experimental Design and Method Selection:
The study utilized UAV image blocks captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. The geometric and radiometric performance of these cameras was evaluated.
2：5, and 5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. The geometric and radiometric performance of these cameras was evaluated.
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: The test area was a peat production area in Okssuo, southern Finland. Ground reference data included 13 ground control points (GCPs) and 44 peat samples for moisture and reflectance measurements.
3：List of Experimental Equipment and Materials:
Equipment included FPI VNIR and SWIR hyperspectral frame cameras, a consumer RGB camera (Samsung NX300), and UAV platforms (MikroKopter autopilot and Droidworx AD-8 extended frame, Tarot 960 foldable frame with Tarot 5008 brushless electric motors).
4：Experimental Procedures and Operational Workflow:
Image blocks were captured under sunny, clear, and windless conditions. The geometric and radiometric processing included sensor corrections, determination of the geometric imaging model, dense image matching for DSM creation, and radiometric modeling for reflectance transformation.
5：Data Analysis Methods:
The accuracy of geometric processing was assessed using independent check points and DSM evaluations. Radiometric processing included empirical corrections for the SWIR camera. Moisture estimation was performed using linear correlations and support vector machine (SVM) analysis.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
