Introducing the ButterflEYE LS
Cubert GmbH, the leading provider of real time, non-scanning, imaging spectrometers, that combines the simplicity of a point and shoot camera with the precision of hyperspectral imaging, teamed up earlier this year with VITO’s Remote Sensing Unit. The idea was to develop the COSI Cam, a low cost, revolutionary high ground resolution hyperspectral imager, utilising the novel hyperspectral imaging chip developed by IMEC (Belgium).
After a few months of designing and developing the sensor, the hard work of the VITO and Cubert team finally paid off as the first prototype of the COSI Cam took to the skies over Southern Germany this week.
Utilising the dedicated test site next to the Cubert HQ, with access to corn crops, apple orchards and various types of vegetation. The COSI cam was mounted onto a small UAV, this enables the sensor to monitor hundreds of hectares in a single flight. The COSI Cam is designed to meet the fast growing remote sensing market that covers a vast range of applications, such as; agriculture, precision farming, yield prediction, forest management, biomass, fertiliser management and crop health to name but a few key examples.
Initial results are extremely promising thanks to the fast and easy imaging method combined with a reliable processing developed by VITO to gain ready to use action and information maps.
VITO Remote Sensing and Cubert have come to an agreement to commercialise the system and it will be included into the Cubert hyperspectral imaging system portfolio as the first high resolution hyperspectral mapping device. Cubert will brand the new sensor under their ButterflEYE range of imaging products, the ButterflEYE LS.
“We helped design the new COSI Cam to complement our performance leading FireflEYE and ButterflEYE line of sensors and with the VITO mapping software this will truly be a class leading fully integrated sensor”, explained Cubert CEO Rene Michels.
Technology of the ButterflEYE LS
The newly branded ButterflEYE LS is based on linear filter-on-chip spectral imager technology. The imager operates at high frame rates on unmanned aerial vehicles (UAV’s) to generate over 160 spectral channel hyperspectral maps with a ground resolution of up to 9cm. The imager is built with ease of use in mind, as there is a control panel for computerless operation, all data is stored on camera SSD hard drive, combined with wireless remote operation.
Figure 1. Imaging concept of the COSI-Cam with linear variable filter (left), a single raw image of the COSI-Cam (right)
Conceptually the system is very similar to a standard digital camera as it acquires 2-dimensional images of the scene. However, the hyperspectral capability, which allows to sense the visible and near infrared spectral range through narrow bands, is realized using a disruptive thin film filter technology which is directly deposited at the image sensor chip of the camera. The spectral bands are arranged per line, with groups of 5 to 8 adjacent lines having the same spectral response.
The imaging concept of the camera is shown in figure 1. The thin film filters were deposited on a 2 megapixels high sensitivity CMOS image sensor by the micro-electronics laboratory Imec giving a continuous coverage of the 600 to 900 nm spectral range with 160 narrow band filter responses (FWHM 5 to 10 nm) .
A unique property of the specific camera design and processing approach is that it is able to produce maps with a very high spatial resolution (e.g. 9cm from 180m altitude). As a result, the system allows to support single plant monitoring in, for instance small experimental field plots of a few m² each and covering a total area of about 1 ha, as has been demonstrated using rotary wing Remote Piloted Aircraft Systems (RPAS). Embarked on a suitable fixed wing RPAS system the system is able to cover also larger areas of hundreds of ha in a single flight mission at resolutions of 10 cm which is more suitable for operational monitoring.
Image processing software of the the ButterflEYE LS
The image processing workflow is developed by VITO and involves several steps:
- hyperspectral datacube generation
- raster calculation and compositing
During pre-processing the collected data are validated using quality checks on the flight and camera metadata and the raw images. Also, the images are enhanced and georeferenced using the on-board GPS. After that, the hyperspectral datacube generation is initiated. It includes aerial triangulation, bundle block adjustment, camera calibration and point cloud generation algorithms (Sima et al., 2016). Next, the hyperspectral bands are reconstructed from the individual images and radiometrically corrected. Lastly, the spectral indices and false-colour composites are derived from raster calculation and compositing of the hyperspectral datacube.
Figure 2. Flowchart of the image processing software workflow.
At VITO, an operational cloud processing environment has been set up, to allow users to upload their data on the local processing cluster with advanced processing nodes (12 CPU’s, 64GB RAM, GPU support) for high speed processing. The entire workflow runs semi-automated: manual interactions are currently only needed for geometric and spectral ground control point identification and visual end-product validation.
The ButterflEYE LS image processing software supports four standard output data products:
- digital surface model
- hyperspectral datacube
- false-colour image composite
- spectral index map
The digital surface model (DSM) represents the 3D model of the terrain’s surface. The image processing software exports the digital surface model in the user defined coordinate system and format. For enhanced absolute spatial accuracy, ground control points can be introduced.
The hyperspectral datacube contains the surface reflection orthomosaic images of each single spectral band. The spectral range of the ButterflEYE LS is 450 to 950 nm, while the spectral resolution is about 5 to 10 nm. For visual display, each band of the hyperspectral cube may be displayed one band at a time as a grey scale image, or in combination of three bands at a time as a colour composite image. The false-colour image composite provides a quick overview of the terrain vegetation. The false-colour composition used is:
R = band 41 (800 nm; NIR)
G = band 15 (670 nm; red)
B = band 2 (605 nm; green)
In this type of false-colour composite images, vegetation appears in different shades of red depending on the types and conditions of the vegetation, since it has a high reflectance in the NIR band. Clear water appears dark-bluish (higher green band reflectance), while turbid water appears cyan (higher red reflectance due to sediments) compared to clear water. Bare soils, roads and buildings may appear in various shades of blue, yellow or grey, depending on their composition (Liew, 2001).
The spectral index map is a graphical indicator for specific terrain analysis. Different bands may be combined to accentuate e.g. the vegetated areas. One of the oldest combinations is the Normalized Difference Vegetation Index (NDVI) . The combination of its normalized difference formulation and use of the highest absorption and reflectance regions of chlorophyll make it robust over a wide range of conditions. It can, however, saturate in dense vegetation conditions when Leaf Area Index LAI becomes high. NDVI is computed by band 15 (670 nm) and band 41 (800 nm):
NDVI = (band 41 – band 15) / (band 41 + band 15) (1)
The value of this index ranges from -1 to 1. The common range for green vegetation is 0.2 to 0.8 (Roose et al., 1973).
Another used vegetation index is the Red Edge Normalized Difference Vegetation Index (ReNDVI), which is a modification of the traditional broadband NDVI and differs by using bands along the red edge, instead of the main absorption and reflectance peaks. Applications include precision agriculture, forest monitoring, and vegetation stress detection. The ReNDVI capitalizes on the sensitivity of the vegetation red edge to small changes in canopy foliage content, gap fraction, and senescence. ReNDVI is computed by band 22 (705 nm) and band 31 (750 nm):
ReNDVI = (band 31 – band 22) / (band 31 + band 22) (2)
The value of this index ranges from -1 to 1. The common range for green vegetation is 0.2 to 0.9 (Gitelson et al., 1994 and Sims et al., 2002). Besides these two examples, many more relevant spectral indices can be generated from the hyperspectral datacube, highlighting different biophysical aspects of the soil, crop growth and crop condition.
After the flight the software solution, provided by VITO generates a hyperspectral ground map. The whole calculation is processed conveniently in the cloud. The processing does not stop at the provision of spectral datamaps, Cubert has the philosophy of Acquire, Process, Interpret and Action, helping support customers understand the data collected.
“The global demand for food is driving the expansion of the precision farming market, and is expected to grow to $4.80 Billion USD by 2020 hence the importance of getting a balance of the right imager with the right software at the right price, which we have achieved with the ButterflEYE LS” Cuberts CEO, Michels noted.
The ButterflEYE LS imager will be available to order from Cubert in the autumn with first deliveries being made in in December 2016.