AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems (original) (raw)

2009, Advances in Geoscience and Remote Sensing

resolution and hour-level temporal resolution) from low altitudes with less interference from clouds. Small UAVs combined with ground and orbital sensors can even form a multi-scale remote sensing system. UAVs equipped with imagers have been used in several agricultural remote sensing applications for collecting aerial images. High resolution red-green-blue (RGB) aerial photos can be used to determine the best harvest time of wine grapes ]. Multispectral images are also shown to be potentially useful for monitoring the ripeness of coffee ]. Water management is still a new area for UAVs, but it has more exact requirements than other remote sensing applications: real-time management of water systems requires more and more precise information on water, soil and plant conditions, for example, than most surveillance applications. Most current UAV remote sensing applications use large and expensive UAVs with heavy cameras (in the range of a kilogram). Images from reconfigurable bands taken simultaneously can increase the final information content of the imagery and significantly improve the flexibility of the remote sensing process. Motivated by the above remote sensing problem, AggieAir, a band-configurable small UASbased remote sensing system has been developed in steps at Center for Self Organizing and Intelligent Systems (CSOIS) together with Utah Water Research Lab (UWRL), Utah State University. The objective of this chapter is to present an overview of the ongoing research on this topic. The chapter first presents a brief overview of the unmanned aircraft systems focusing on the base of the whole system: autopilots. The common UAS structure is introduced. The hardware and software aspects of the autopilot control system are then explained. Different types of available sensor sets and autopilot control techniques are summarized. Several typical commercial off-the-shelf and open source autopilot packages are compared in detail, including the Kestrel autopilot from Procerus, Piccolo autopilot from CloudCap, and the Paparazzi open source autopilot etc. The chapter then introduces AggieAir, a small and low-cost UAS for remote sensing. Ag-gieAir comprises of a flying-wing airframe as the test bed, the OSAM-Paparazzi autopilot for autonomous navigation, the Ghost Foto image system for image capture, the Paparazzi ground control station (GCS) for real time monitoring, and the gRAID software for image processing. AggieAir is fully autonomous, easy to manipulate, and independent of a runway. AggieAir can carry embedded cameras with different wavelength bands, which are low-cost but have high spatial resolution. These imagers mounted on UAVs can form a camera array to perform multi-spectral imaging with reconfigurable bands, depending on the objectives of the mission. Developments of essential subsystems, such as the UAV autopilot, imaging payload subsystem, and image processing subsystem, are introduced in detail together with some experimental results to show the orthorectification accuracy. Several typical example missions together with real UAV flight test results are focused in Sec.3 including land survey, water area survey, riparian applications and remote data collection. Aerial images and stitched maps showed the effectiveness of the whole system. The future direction is more accurate orthorectification method and band-reconfigurable multi-UAV-based cooperative remote sensing for real-time water management and distributed irrigation control.