Capability of geometric features to classify ships in SAR imagery

Haitao Lang; Siwen Wu; Quan Lai; Li Ma

doi:10.1117/12.2241375

18 October 2016 Capability of geometric features to classify ships in SAR imagery

Haitao Lang, Siwen Wu, Quan Lai, Li Ma

Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 1000415 (2016) https://doi.org/10.1117/12.2241375
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Ship classification in synthetic aperture radar (SAR) imagery has become a new hotspot in remote sensing community for its valuable potential in many maritime applications. Several kinds of ship features, such as geometric features, polarimetric features, and scattering features have been widely applied on ship classification tasks. Compared with polarimetric features and scattering features, which are subject to SAR parameters (e.g., sensor type, incidence angle, polarization, etc.) and environment factors (e.g., sea state, wind, wave, current, etc.), geometric features are relatively independent of SAR and environment factors, and easy to be extracted stably from SAR imagery. In this paper, the capability of geometric features to classify ships in SAR imagery with various resolution has been investigated. Firstly, the relationship between the geometric feature extraction accuracy and the SAR imagery resolution is analyzed. It shows that the minimum bounding rectangle (MBR) of ship can be extracted exactly in terms of absolute precision by the proposed automatic ship-sea segmentation method. Next, six simple but effective geometric features are extracted to build a ship representation for the subsequent classification task. These six geometric features are composed of length (f₁), width (f₂), area (f₃), perimeter (f₄), elongatedness (f₅) and compactness (f₆). Among them, two basic features, length (f₁) and width (f₂), are directly extracted based on the MBR of ship, the other four are derived from those two basic features. The capability of the utilized geometric features to classify ships are validated on two data set with different image resolutions. The results show that the performance of ship classification solely by geometric features is close to that obtained by the state-of-the-art methods, which obtained by a combination of multiple kinds of features, including scattering features and geometric features after a complex feature selection process.

Conference Presentation

Citation Download Citation

Haitao Lang, Siwen Wu, Quan Lai, and Li Ma "Capability of geometric features to classify ships in SAR imagery", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000415 (18 October 2016); https://doi.org/10.1117/12.2241375

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available