PROCEEDINGS ARTICLE | October 13, 2014
Proc. SPIE. 9250, Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing II
KEYWORDS: 3D acquisition, Cameras, Sensors, Remote sensing, 3D modeling, Vegetation, Data acquisition, Laser scanners, Distance measurement, 3D metrology
Automatic change detection in 3D environments requires the comparison of multi-temporal data. By comparing current
data with past data of the same area, changes can be automatically detected and identified. Volumetric changes in the scene
hint at suspicious activities like the movement of military vehicles, the application of camouflage nets, or the placement
of IEDs, etc. In contrast to broad research activities in remote sensing with optical cameras, this paper addresses the topic
using 3D data acquired by mobile laser scanning (MLS). We present a framework for immediate comparison of current
MLS data to given 3D reference data. Our method extends the concept of occupancy grids known from robot mapping,
which incorporates the sensor positions in the processing of the 3D point clouds. This allows extracting the information
that is included in the data acquisition geometry. For each single range measurement, it becomes apparent that an object
reflects laser pulses in the measured range distance, i.e., space is occupied at that 3D position. In addition, it is obvious
that space is empty along the line of sight between sensor and the reflecting object. Everywhere else, the occupancy of
space remains unknown. This approach handles occlusions and changes implicitly, such that the latter are identifiable by
conflicts of empty space and occupied space. The presented concept of change detection has been successfully validated
in experiments with recorded MLS data streams. Results are shown for test sites at which MLS data were acquired at
different time intervals.
