Abstract
In this paper, we present an unsupervised scheme aimed at segmentation of laser radar (LADAR) imagery for Automatic Target Detection. A coding theoretic approach implements Rissanen's concept of Minimum Description Length (MDL) for estimating piecewise homogeneous regions. MDL is used to penalize overly complex segmentations. The intensity data is modeled as a Gaussian random field whose mean and variance functions are piecewise constant across the image. This model is intended to capture variations in both mean value (intensity) and variance (texture). The segmentation algorithm is based on an adaptive rectangular recursive partitioning scheme. We implement a robust constant false alarm rate (CFAR) detector on the segmented intensity image for target detection and compare our results with the conventional cell averaging (CA) CFAR detector.
Original language | English (US) |
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Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
Editors | F.A. Sadjadi |
Pages | 86-94 |
Number of pages | 9 |
Volume | 4379 |
DOIs | |
State | Published - 2001 |
Event | Automatic Target Recognition XI - Orlando, FL, United States Duration: Apr 17 2001 → Apr 20 2001 |
Other
Other | Automatic Target Recognition XI |
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Country/Territory | United States |
City | Orlando, FL |
Period | 4/17/01 → 4/20/01 |
Keywords
- Adaptive Recursive Partitioning
- Automatic Target Detection
- CFAR
- Laser Radar
- MDL
- Segmentation
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics