Assessment of Polarimetric SAR Interferometry for Improving Ship Classification based on Simulated Data

Abstract

This paper uses a complete and realistic SAR simulation processing chain, GRECOSAR, to study the potentialities of Polarimetric SAR Interferometry (POLInSAR) in the development of new classification methods for ships. Its high processing efficiency and scenario flexibility have allowed to develop exhaustive scattering studies. The results have revealed, first, vessels' geometries can be described by specific combinations of Permanent Polarimetric Scatterers (PePS) and, second, each type of vessel could be characterized by a particular spatial and polarimetric distribution of PePS. Such properties have been recently exploited to propose a new Vessel Classification Algorithm (VCA) working with POLInSAR data, which, according to several simulation tests, may provide promising performance in real scenarios. Along the paper, explanation of the main steps summarizing the whole research activity carried out with ships and GRECOSAR are provided as well as examples of the main results and VCA validation tests. Special attention will be devoted to the new improvements achieved, which are related to simulations processing a new and highly realistic sea surface model. The paper will show that, for POLInSAR data with fine resolution, VCA can help to classify ships with notable robustness under diverse and adverse observation conditions.

Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

“…However, with increasing maritime problems by vessels and increasing SAR resolution in recent years, need has been raised by coast guards and related organizations for ship identification, which initiated the research and development of integrated ship detection and identification systems. There are three main approaches to ship identification: (1) Decision-based classification [544][545][546]; (2) Classification by pattern matching [547]; and (3) Multi-channel classification [516,[548][549][550][551][552], excluding the use of ISAR (Inverse SAR) [553]. Probably, the most practical algorithm at present may be the decision-based classification by GMV Aerospace and Defense in Spain [546].…”

A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

“…Experiments showed a low number of vectors could lead to an overestimation of the classification rate, and an excessive number of patterns would make quite similar geometries to be classified in different classes [37]. …”

“…In PE-ATR, many scientists work with performance models and/or evaluation models [ 33 ]. The existing performance models and evaluation models can be classified as: (a) models based on probability, statistics, and random processes [ 9 , 34 ]; (b) models based on Bayesian approach [ 35 ]; (c) models based on information theory approach [ 35 ]; (d) subsystem performance models [ 36 ]; (e) other performance models [ 37 ].…”

“…To study the potentialities of polarimetric SAR interferometry (POLInSAR) in developing a new classification methods for ships, performance evaluation has been performed to accomplish a trade-off between geometry description accuracy and method robustness in reference feature vectors (or patterns) [ 37 ]. Experiments showed a low number of vectors could lead to an overestimation of the classification rate, and an excessive number of patterns would make quite similar geometries to be classified in different classes [ 37 ].…”

Ship Classification with High Resolution TerraSAR-X Imagery Based on Analytic Hierarchy Process

“…Knapskog et al [9] investigated the classification of ships based on 3D model and the contour feature. Margarit and Mallorqui [10] extracted scattering center feature for ship classification. They also applied localized RCS features to ship classification and used ships' structural information with ENVISAT data [2,11].…”