A hierarchical propelled fusion strategy for SAR automatic target recognition

Zongyong Cui,Jianyu Yang,Jilan Feng,Zongjie Cao

doi:10.1186/1687-1499-2013-39

Abstract

Abstract Synthetic aperture radar (SAR) automatic target recognition (ATR) is playing a very important role in military and civil field. Much work has been done to improve the performance of SAR ATR systems. It is well-known that ensemble methods can be used for improving prediction performance. Thus recognition using multiple classifiers fusion (MCF) has become a research hotspot in SAR ATR. Most current researchers focus on the fusion methods by parallel structure. However, such parallel structure has some disadvantages, such as large time consumption, features attribution conflict and low capability on confuser recognition. A hierarchical propelled strategy for multi-classifier fusion (HPSMCF) is proposed in this paper. The proposed HPSMCF has the characters both of series and parallel structure. Features can be used more effective and the recognition efficiency can be improved by extracting features and fusing the probabilistic outputs in a hierarchical propelled way. Meanwhile, the confuser recognition can be achieved by setting thresholds for the confidence in each level. Experiments on MSTAR public data demonstrate that the proposed HPSMCF is robust for variant recognition conditions. Compared with the parallel structure, HPSMCF has better performance both on time consumption and recognition rate.

Highlights

Synthetic aperture radar (SAR) is playing an important role both in national defense and civil applications, because SAR can work in all weather and day/night conditions
After that many researchers have done lots of work to improve the performance of SAR automatic target recognition (ATR) systems
It is well-known that ensemble methods can be used for improving prediction performance [4]

Summary

Introduction

SAR is playing an important role both in national defense and civil applications, because SAR can work in all weather and day/night conditions. After that many researchers have done lots of work to improve the performance of SAR ATR systems. It is well-known that ensemble methods can be used for improving prediction performance [4]. Develop a common theoretical framework for combining classifiers [5]. In 2010, Lior Rokach proposes an ensemble system which is composed of several independent base-level models [4]. These base-level classifiers are respectively constructed using different techniques and methods. With the development of MCF technology, MCF has been extensively applied in many areas, such as character recognition [6], multi-sensor data classification [7] and SAR ATR [8,9]

Objectives

Methods

Findings

Conclusion