Here, I report a brand new filter-based modification of a 4K Camera, The Hasselblad 20MP Camera onboard the DJI Mavic 2 Pro, to develop an ultraviolet (UV) imaging system for remote sensing.
This was achieved via testing and adapting new quartz-based Ultraviolet imaging filters as well as thin film solar-filters in conjunction with commercial cameras modified using "hot-mirror" filter removal.
The Hasselblad cameras used in the Mavic 2 Pro contain one of the best passive imaging,
complementary metal-oxide semiconductor (CMOS) sensor, A 1-inch sensor with 20 megapixels which can be set to image using very high exposure to record in the Near-UV.
The utility of these devices is demonstrable for applications at wavelengths as low as 310 nm,
in particular for sensing vegetation in this spectral region. For this a novel UV-based remote sensing
classification index has been developed for use in experimental Ultraviolet aerial imaging using
drones.
Given the relatively very low cost of these units as compared with other cameras in this field of imaging, and the fact they are integrated on a superb platform for deployment, a semi-autonomous aerial vehicle, they are suitable for widespread proliferation in the field of environmental monitoring
in a variety of UV imaging applications, e.g., in atmospheric science, vulcanology, oceanography, forensics, monitoring of industry and utility structures (in particular powerlines and smokestacks), fluorescent tracer measurements and general surface measurements.
This was achieved via testing and adapting new quartz-based Ultraviolet imaging filters as well as thin film solar-filters in conjunction with commercial cameras modified using "hot-mirror" filter removal.
The Hasselblad cameras used in the Mavic 2 Pro contain one of the best passive imaging,
complementary metal-oxide semiconductor (CMOS) sensor, A 1-inch sensor with 20 megapixels which can be set to image using very high exposure to record in the Near-UV.
The utility of these devices is demonstrable for applications at wavelengths as low as 310 nm,
in particular for sensing vegetation in this spectral region. For this a novel UV-based remote sensing
classification index has been developed for use in experimental Ultraviolet aerial imaging using
drones.
Given the relatively very low cost of these units as compared with other cameras in this field of imaging, and the fact they are integrated on a superb platform for deployment, a semi-autonomous aerial vehicle, they are suitable for widespread proliferation in the field of environmental monitoring
in a variety of UV imaging applications, e.g., in atmospheric science, vulcanology, oceanography, forensics, monitoring of industry and utility structures (in particular powerlines and smokestacks), fluorescent tracer measurements and general surface measurements.
I am beginning experimental testing of this technology in Gran Canaria over the next few months. I have already begun to construct test image datasets for analysis using a prototype Normalized UV Absorption Index (NUVAI)
With this index I hope to be able to classify vegetation and surface features based on their UV absorption characteristics and compare with the NDVI taken using the same camera with my already extensively tested Infrared Filters.
Ultraviolet Drone Aerial Image
Using Python coding I have digitally processed some of the test images already and hope to perform similar work as used in my near-infrared (NIR) drone research.
Ultraviolet Reflectance with an NDVI-style Key
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