The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems’ SOCET SET classical commercial photogrammetric software and another is built using Microsoft^®’s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation.

The objective of this investigation was to study the use of a new type of a low-weight unmanned aerial vehicle (UAV) imaging system in the precision agriculture. The system consists of a novel Fabry-Perot interferometer based hyperspectral camera and a high-resolution small-format consumer camera. The sensors provide stereoscopic imagery in a 2D frame-format and they both weigh less than 500 g. A processing chain was developed for the production of high density point clouds and hyperspectral reflectance image mosaics (reflectance signatures), which are used as inputs in the agricultural application. We demonstrate the use of this new technology in the biomass estimation process, which is based on support vector regression machine. It was concluded that the central factors influencing on the accuracy of the estimation process were the quality of the image data, the quality of the image processing and digital surface model generation, and the performance of the regressor. In the wider perspective, our investigation showed that very low-weight, low-cost, hyperspectral, stereoscopic and spectrodirectional 3D UAV-remote sensing is now possible. This cutting edge technology is powerful and cost efficient in time-critical, repetitive and locally operated remote sensing applications.

Data collection under highly variable weather and illumination conditions around the year will be necessary in many applications of UAV imaging systems. This is a new feature in rigorous photogrammetric and remote sensing processing. We studied performance of two georeferencing and point cloud generation approaches using image data sets collected in four seasons (winter, spring, summer and autumn) and under different imaging conditions (sunny, cloudy, different solar elevations). We used light, quadrocopter UAVs equipped with consumer cameras. In general, matching of image blocks collected with high overlaps provided high quality point clouds. All of the before mentioned factors influenced the point cloud quality. In winter time, the point cloud generation failed on uniform snow surfaces in many situations, and during leaf-off season the point cloud generation was not successful over deciduous trees. The images collected under cloudy conditions provided better point clouds than the images collected in sunny weather in shadowed regions and of tree surfaces. On homogeneous surfaces (e.g. asphalt) the images collected under sunny conditions outperformed cloudy data. The tested factors did not influence the general block adjustment results. The radiometric sensor performance (especially signal-to-noise ratio) is a critical factor in all weather data collection and point cloud generation; at the moment, high quality, light weight imaging sensors are still largely missing; sensitivity to wind is another potential limitation. There lies a great potential in low flying, low cost UAVs especially in applications requiring rapid aerial imaging for frequent monitoring.