Assessment of preprocessing techniques in a model-based automatic target recognition algorithm for the SAMPLE dataset

• This article investigates basic preprocessing techniques to improve classification accuracy in the context of Automatic Target Recognition (ATR) of non-cooperative targets in Synthetic Aperture Radar (SAR) images. Preprocessing techniques are considered in synthetic data providing different inputs to a model-based classification algorithm. Experiments with preprocessing techniques such as area reduction, morphological transformations, and speckle filtering were run using ten target classes of the SAMPLE dataset. The classification is performed in measure data using scattering centers as features. The results reveal that the original image without any preprocessing techniques reached the best classification performance. However, investigations with other classifiers that use different features may benefit from such preprocessing techniques. © 2022 SPIE.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Automatic target recognition

Classification (of information)

Image enhancement

Radar imaging

Radar target recognition

Classification accuracy

Model-based classifications

Model-based OPC

Non-cooperative target

Pre-processing techniques

Preprocessing

Scattering centers

Synthetic aperture radar images

Synthetic data

Target recognition algorithms

Synthetic aperture radar

classification

Publication and Content Type

ref (subject category)

kon (subject category)