Unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU) navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification.

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ABSTRACT:Small fixed wing and rotor-copter unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification and precision agriculture applications. Various sensors operating in the non-visible spectrum such as multispectral, hyperspectral and thermal sensors can be used as payloads. This work presents a preliminary study on the use of unmanned aerial vehicle equipped with a compact spectrometer for land cover type characterization. When calibrated, the measured spectra by the UAV spectrometer can be processed and compared reference data to generate georeferenced reflection spectra enabling the identification, classification and characterization of land cover elements. For this case study we used a DJI Flamewheel F550 hexacopter and the FLAME-NIR spectrometer for hyperspectral measurements. The calibration of the spectrometer is described as well the approach to determine its spatial footprint. The spectrometer spectral exposure labeled ground point can be used to determine the land cover classification. Preliminary results of a case-study are presented.

ABSTRACT:The purpose of this scientific survey is to support the research being conducted at York University in the field of spectroscopy and nanosatellites using Argus 1000 micro-spectrometer and low cost unmanned aerial vehicle (UAV) system.On the CanX-2 mission, the Argus spectrometer observes reflected infrared solar radiation emitted by Earth surface targets as small as 1.5 km within the 0.9-1.7 µm range. However, limitations in the volume of data due to onboard power constraints and a lack of an onboard camera system make it very difficult to verify these objectives using ground truth. In the last five years that Argus has been in operation, we have made over 200 observations over a series of land and ocean targets.We have recently examined algorithms to improve the geolocation accuracy of the spectrometer payload and began to conduct an analysis of soil health content using Argus spectral data. A field campaign is used to obtain data to assess geolocation accuracy using coastline crossing detection and to obtain airborne bare soil spectra in ground truth form. The payload system used for the field campaign consists of an Argus spectrometer, optical camera, GPS, and attitude sensors, integrated into a low-cost, unmanned aerial vehicle (UAV), which will be presented along with the experimental procedure and field campaign results.