4-Band Data Acquisition at Faster Speeds
A data acquisition project was performed over a 160 square kilometers area. The imagery obtained included 4-band images of high spatial resolution (GSD 2.5 cm/pix) and a point cloud density of 30 points/m2.
The project’s biggest challenge was the precise detection of vegetation over the entire area, which was made possible through the efficient implementation of the multi-sensor system. The coverage of the large area required the use of a more efficient and faster aircraft.
An integrated hybrid sensor system, using high-definition Phase One cameras, on an efficient aircraft platform allows the final product to be fully adapted for efficient automation and improved data processing.
Easy integration with multi-sensor system
Phase One cameras integrate easily with other components into a single hybrid sensor system. MapSoft operated the system with a single flight management system and all sensors of the hybrid system used a single GNNS / INS system. As a result, acquired data was easily combined, highly accurate, and of high quality.
The geometric and radiometric quality of imagery from the Phase One cameras was perfect due to the high quality of the CMOS sensor and short shutter speeds. The shutter synchronization of the two calibrated cameras (RGB + NIR) meant that 4-band images obtained have the matching details with subpixel accuracy. This caused no image blurring regardless of the 2.5 cm/pix GSD, which led to accurate detection of the boundaries on the data used.
Hybrid sensor system improves data collection efficiency
Mapsoft d.o.o. is a geomatics company from Belgrade, Serbia, specializing in data collection via photogrammetric methods and developing GIS solutions in Southeast Europe for almost 20 years.
To become more efficient in their data acquisition, the company uses an integrated hybrid sensor system installed on the SOMAG GSM 3000 gyrostabilizer, consisting of a Teledyne Optech Galaxy LiDAR scanner and two Phase One 100MP cameras (RGB and NIR). MapSoft installs the system into a Serbian AeroEast Europe SILA 750 aircraft (general aviation type), explicitly designed to take aerial systems for photogrammetric projects.
Post-processing with Perfect Results
During point cloud processing from georeferenced images, each point is assigned a NIR value in addition to the RGB attributes. This makes it possible to use the NDVI index in addition to the standard point cloud classification procedure for vegetation classification. The high geometric accuracy of the images and point cloud makes it possible to precisely assign attributes to the point cloud from 4-band images. As a result, vegetation was automatically detected with a much higher degree of reliability and accuracy compared to the classification without the use of NIR channels. The vegetation mapping process produced 6 types of categories: RGB; CIR; NDVI; classified; ground vegetation; and vegetation.
Along with other data (2.5 cm/pix orthophoto, trees, shrubs, flower gardens, etc.) of the established GIS of green areas, vegetation data is an excellent spatial basis for recording, preserving, maintaining, protecting, and planning green areas and city natural spaces in general. That defines, measures, and sets standards for city management and development.
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