Three-dimensional sensors based on Laser Radar (LADAR) technology possess vast potential for the future battlefield.
This work presents an algorithm for the recognition of T62 and T72 tanks from 3D imagery.
The algorithm consists of several stages:
a) Pre-processing of LADAR images to remove range noise and to determine ground level.
b) Segmentation to extract regions that fulfill certain pre-defined conditions.
c) Extraction of specific tank features from each region.
d) Applying a Fuzzy Logic classifier on the feature vector to discriminate between T62 or T72 tanks
and other type of targets or natural clutter.
A commercial airborne LADAR sensor was used to acquire images from an area of 40 square kilometers with a measurement
density of 20 pixels per square meter and a range noise of 15 cm (1 sigma). The images included more than a hundred man-made objects (tanks, armored personnel carriers, trucks, cranes)along with natural clutter (vegetation and boulders). Among the targets were 18 tanks,
two of which were covered with a camouflage net. The algorithm recognized the 16 uncovered tanks with a False Alarm Rate (FAR) of 0.025 per square kilometer. This FAR value is better than the respective FAR values derived for 2D Imaging where Automatic Target Recognition (ATR) techniques are applied.
These results show promise for automatic recognition of various targets employing LADAR sensors.
Automatic delineation of buildings is very attractive for both civilian and military applications. Such applications include general mapping, detection of unauthorized constructions, change detection, etc. For military applications, high demand exists for accurate building change updates, covering large areas, and over short time periods. We present two algorithms coupled together. The
height image algorithm is a fast coarse algorithm operating on large areas. This algorithm is capable of defining blocks of buildings and regions of interest. The point-cloud algorithm is a fine, 3D-based, accurate algorithm for building delineation. Since buildings may be
separated by alleys, whose width is similar or narrower than the LADAR resolution, the height image algorithm marks those crowded buildings as a single object. The point-cloud algorithm separates and accurately delineates individual building boundaries and building sub-sections utilizing roof shape analysis in 3D. Our focus is on the ability to cover large areas with accuracy and high rejection of non-building objects, like trees. We report a very good detection performance with only few misses and false alarms. It is believed that LADAR measurements, coupled with good segmentation algorithms, may replace older systems and methods that require considerable manual work for such applications.