Method and system for automatic classification of objects
AssigneeTelefonaktiebolaget L M Ericsson (publ)
InventorsHilde Valo, Jens Fredrik Hjelmstad, Atle Onar Knapskog
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Abstract
A new method has been developed as an attempt to improve speed and robustness of existing ISAR classification methods. The new method produces a set of silhouettes of possible models in a 3D model database. The set of silhouettes of each model views the model from various viewing angles, as the target dimensions will vary as it is viewed from different angles. The silhouettes are stored as a training set. Classification is done by comparing the silhouette of the target with the set of silhouettes in the training set. The silhouettes are calculated prior to the silhouette matching.
Claims18
A method of automatic classification of an observed object in Inverse Synthetic Aperture Radar (ISAR) image, comprising the steps of:
- extracting a silhouette of the observed object,
- extracting a length parameter for the silhouette of the observed object,
- retrieving, based on the extracted length, training silhouettes for model objects having a similar length corrected for range from a database with a range of training silhouettes representing model objects of different classes and with different orientation angles,
- comparing the silhouette of the observed object with said retrieved training silhouettes, and
- classifying the observed object as being of the model object class with highest correlation between the silhouettes of the observed and model objects.
A system for automatic classification of an observed object in an Inverse Synthetic Aperture Radar (ISAR) image including a sensor supplying at least one image showing an observed object of interest, comprising:
- a pre-processing unit for extracting a silhouette of said observed object and extracting a length parameter for the silhouette of the observed object,
- a database with stored training silhouettes of classified model objects in several orientations, and
- a classification unit for selecting, based on the extracted length, a set of training silhouettes of model objects having similar length parameters corrected for range as in the observed object and retrieve said selected set from the database,
- said classification unit for comparing the silhouette of the observed object with said set of selected training silhouettes, and for classifying said observed object as being of the class corresponding to the training silhouettes which best matches the silhouette of the observed object.
Dependencies are inferred from the claim text.