<title>Classification of ships in airborne SAR imagery using backpropagation neural networks</title>

Abstract

This paper proposes using a backpropagation (BP) neural network for the classification of ship targets in airborne synthetic aperture radar (SAR) imagery. The ship targets consisted of 2 destroyers, 2 cruisers, 2 aircraft carriers a frigate and a supply ship. A SAR image simulator was employed to generate a training set, a validation set, and a test set for the BP classifier.The features required for classification were extracted from the SAR imagery using three different methods. The first method used a reduced resolution version of the whole SAR image as input to the BP classifier using simple averaging. The other two methods used the SAR image range profile either before or after a local-statistics noise filtering algorithm for speckle reduction. Performance on an extensive test set demonstrated the performance and computational advantages of applying the neural classification approach to targets in airborne SAR imagery. Improvements due to the use of multi-resolution features were also observed.

Ship classification in TerraSAR-X SAR images based on classifier combination

“…[5][6][7][8][9] and references therein). The authors in [5] proposed using a back propagation (BP) neural network classifier and conducted the classifier on simulated dataset. In [6], Robert Klepko utilizes a combination of various features to recognize ships with the aid of syntactic pattern recognition algorithm.…”

“…There have been a handful of prior works in the literature studying the design of classifier to recognize ships in SAR images (see e.g. [5][6][7][8][9] and references therein). The authors in [5] proposed using a back propagation (BP) neural network classifier and conducted the classifier on simulated dataset.…”

Enhanced SVM versus Several Approaches in SAR Target Recognition

“…The third category employs neural networks [4] [5]. Two of the most popular employed network architectures are the multilayer feedforward network and the probabilistic neural network (PNN).…”

Ship Classification Based on MSHOG Feature and Task-Driven Dictionary Learning with Structured Incoherent Constraints in SAR Images

“…As one of the most important steps, ship classification has attracted much interest. Early ship classification research is usually based on simulative SAR ship samples [2]. Subsequently, relatively medium resolution SAR images appeared.…”