FS60C/62C UAV hyperspectral measurement system
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Description
- Dji M350/300RTK is used as the flight bearing platform.
- Ultra-high speed spectral scanning imaging device with high signal-to-noise ratio provides high stability spectral image acquisition.
- The self-developed image processing algorithm with high efficiency and low power consumption can greatly prolong the flight time and reduce the power consumption of the system.
- Through real-time measurement of spectral image information of plants, water bodies, soil and other ground objects, application and precision agriculture, crop growth and yield assessment, forest pest monitoring and fire prevention monitoring, coastline and Marine environment monitoring, lake and watershed environmental
monitoring and other applications. - Compact system design, imaging spectrometer host spectral resolution up to 2.5nm.
- The whole machine consists of: high stability head, hyperspectral imager, embedded data acquisition, processing and storage unit, wireless image transmission system, GPS-RTK navigation system, ground receiving workstation, ground control system, reflectivity calibration board.
Specifications
Hyperspectral camera FS-60C
- Easy to operate, no need for professional drone operator, can achieve
single operation - The ground station can observe the sampling site of the aircraft in real time and set the preview and correction functions of the route data collected point by point by using the ground station: radiometric
correction, reflectivity correction, and area correction support batch
processing - Real-time common vegetation index calculation function
- Support custom real-time analysis model input function
- ENVI is perfectly compatible with multiple data formats
| Lighting Mode | Passive lighting (without light source) |
| Spectroscopic Method | Transmission grating |
| Spectral Range | 400-1000 nm |
| Spectral Band | 1200 |
| Spectral Resolution (FWHM) | 2.5 nm |
| Slit Width | 25 µm |
| Transmission Efficiency | 60% |
| Stray Light | 0.5% |
| Number of Spatial Pixels | Max. 1920 (software configurable) |
| Pixel Size | 5.86 µm |
| Imaging Speed | Full band 128 Hz, after ROI can achieve 3300 Hz |
| Probe | CMOS |
| Signal-to-Noise Ratio | 600/1 |
| Camera Output | USB 3.0 or Gigabit network |
| Camera Interface | C-Mount |
| Attachment | USB 3.0 or Gigabit network |
| ROI | Multiple regions |
| Embedded Data Acquisition | Embedded processor with 512G SSD storage |
| Processing Storage Unit | 512G SSD |
| Dimensions | 20.5 cm x 18.5 cm x 12.9 cm |
| Weight | 1200 g |
| Power Dissipation | 40W |
Hyperspectral camera FS-60C
| Spectroscopic Method | Transmission grating |
| Spectral Range | 900-1700 nm |
| Spectral Channel Number | 1024 |
| Spectral Resolution (FWHM) | 6.5 nm |
| Slit Width | 25 µm |
| Transmission Efficiency | ＞60% |
| Stray Light | ＜0.5% |
| Number of Spatial Pixels | 1280 |
| Pixel Size | 5 µm |
| Imaging Speed | Full band 70 Hz, maximum 1800 Hz |
| Probe | InGaAs |
| Signal-to-Noise Ratio | 600/1 |
| Exportation | Start |
| Camera Interface | C-Mount |
| Attachment | Lens, USB cable, power supply |
| ROI | Multiple regions |
| Built-in Processing Unit | Windows OS, 8GB RAM, 512GB SSD (optional 1TB) integrated with camera |
| Heat Dissipation Mode | Internal air cooling |
| Mode of Operation | Easy to operate, no need for professional drone operation; hand control possible |
Applications
Airborne Hyperspectral Camera Mining Test
The UAV hyperspectral imaging system can quickly collect high-resolution images of the surface by carrying a hyperspectral sensor, capturing the spectral characteristics of different minerals. This enables explorers to quickly identify potential ores and mineralized zones, greatly improving exploration efficiency.
After analyzing the collected data, we get the above group of pictures under
different bands.
The above unsupervised clustering algorithm image is obtained through analysis software
Use Airborne Hyperspectral Camera to Photograph Ponds to Obtain NDVI Images and Images of Potassium Permanganate Distribution in the Water
NDVI Image Analysis and Supervised Clustering after Photographing the Forest Using Airborne Hyperspectral Camera
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