Automatic Target Recognition Using Wavelet-Based Vector Quantization

Abstract

An automatic target recognition classifier is described that uses a set of dedicated vector quantizers (VQs) in the wavelet domain. The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition is used to split this region into several subbands. A dedicated VQ codebook is then generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics. Finally, a path selector was designed to speed up the recognition process at the expense of a tolerable degradation in the recognition rate.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1997
Accession Number
ADA333550

Entities

People

  • Lipchen A. Chan
  • Nasser M. Nasrabadi

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computer-Aided Design
  • Databases
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Image Processing
  • Machine Learning
  • Military Research
  • Neural Networks
  • Parallel Computing
  • Parallel Processing
  • Processing Equipment
  • Recognition
  • Target Classification
  • Target Recognition
  • Target Signatures

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.