Figure 1: Examples of aerial images captured from the kite-based 
imaging platform (taken from approximately 15m altitude). 
strated from experiments at an intertidal rock platform at Cape 
Banks, Sydney, Australia in Section 3. Conclusions and future 
work are discussed in Section 4. 
2 METHODOLOGY 
2.1 Kite-based Image Acquisition 
A kite-based imaging system was built that used a 2.7m wingspan 
conynes-delta kite to lift a fixed, downwards-looking rig hold- 
ing a consumer-grade digital camera. The conynes-delta kite was 
chosen for its stability and lifting capacity in a wide range of wind 
conditions. The camera was suspended from a Picavet rig which 
attached to the line of the kite approximately 10m lower than the 
kite to minimise the impact of wind gust-induced motion of the 
camera. The Picavet provided mechanical levelling of the camera 
during changes in the flying angle of the kite. The camera used 
was a Sony NEX-3 with a 16mm pancake lens which provided an 
imaging field of view of approximately 73-by-52° with a resolu- 
tion of 4592-by-3056 pixels. The camera was chosen as a trade- 
off between the image quality achieved from a full-frame sen- 
sor digital SLR and the light-weight of a small-sensored compact 
digital camera. Figure 1 illustrates example images captured by 
the system at an altitude of approximately 15m from the ground, 
with a coverage footprint of approximately 22-by-15m. 
During data collection, the kite was used to hoist the camera rig 
over the area of interest and the camera programmed to capture 
images at a frequency of approximately one shot per second. The 
kite could be flown at a variety of altitudes between approxi- 
mately 10-100m, based on desired area coverage and ground spa- 
tial resolution and limited by the length of the kite line. The de- 
sired height was achieved using distance markers on the line and 
by approximating the flight angle of the kite. The kite was then 
slowly walked across the terrain allowing multiple overlapping 
images to be captured of the entire area of interest. 
2.2 Image Processing, Feature Extraction and Matching 
After data collection, images were copied from the camera to 
a desktop computer for processing. Images that were affected 
by motion blur during wind gusts or large occlusions of the ter- 
rain (for example images of people moving in the scene) were 
removed manually before the processing began. Scale-Invariant 
Feature Transform (SIFT) features (Vedaldi and Fulkerson, 2008) 
were extracted in each image and matched across all image pairs 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
Figure 2: Steps in Terrain Reconstruction: (a) 3D point cloud 
generated from multi-view stereo, (b) surface triangulation, (c) 
final photo-textured surface model. 
using a kd-tree (Beis and Lowe, 2003) of the features in each im- 
age. Robust detection of feature match outliers was performed us- 
ing epipolar constraints between images (Torr and Murray, 1997). 
SIFT features correspond to distinctive points in the texture of 
surfaces captured in images and were highly suited for use in the 
rocky intertidal environment. Multi-core software implementa- 
tions of these methods were developed in order to process images 
in parallel, speeding up processing times. 
2.3 3D Pointcloud Reconstruction 
A structure-from-motion/ bundle adjustment software package 
(Snavely et al., 2008) was then used to incrementally construct a 
3D point feature map corresponding to the matched image fea- 
ture points while simultaneously estimating camera poses and 
the intrinsic and extrinsic parameters for the camera. The re- 
construction provided a point feature map and relative camera 
poses with unknown scale, absolute rotation and position within 
a geo-referenced coordinate system. Ground control points corre- 
sponding to distinct rock features that were identified in both the 
  
   
   
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