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 results indicated that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production. The FPI hyperspectral technology follows the principles of central perspective imaging, enabling the utilization of latest innovations in computer vision and photogrammetric technologies. The technology is functional in various remote sensing applications and is well suited for developing automatic and autonomous applications.
研究不足
The SWIR range camera was a new prototype with some shortcomings in spectral measurement quality, including missing PRNU calibration and changes in the dark signal during the flight. The geometric performance of the SWIR data set was worse than that of other cameras due to poorer block structure, potentially lower accuracy of the autopilot’s GPS data, smaller image size, and lower image quality.
1：Experimental Design and Method Selection:
The study utilized UAV image blocks captured with ground sample distances (GSDs) of 15,
2：5, and 5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. The FPI technology was used for hyperspectral imaging. 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. Geometric and radiometric processing steps were applied to the imagery to derive quantitative information.
5：Data Analysis Methods:
The geometric accuracy was evaluated using independent check points and DSMs. Radiometric modeling included sensor corrections, atmospheric correction, and BRDF correction. Surface moisture estimation was performed using linear correlations and support vector machine (SVM) regression.
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