Vessel classification in overhead satellite imagery using weighted "bag of visual words"

Shibin Parameswaran; Katie Rainey

doi:10.1117/12.2177779

22 May 2015 Vessel classification in overhead satellite imagery using weighted "bag of visual words"

Shibin Parameswaran, Katie Rainey

Proceedings Volume 9476, Automatic Target Recognition XXV; 947609 (2015) https://doi.org/10.1117/12.2177779
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

Vessel type classification in maritime imagery is a challenging problem and has applications to many military and surveillance applications. The ability to classify a vessel correctly varies significantly depending on its appearance which in turn is affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The difficulty in classifying vessels also varies among different ship types as some types of vessels show more within-class variation than others. In our previous work, we showed that the bag of visual words" (V-BoW) was an effective feature representation for this classification task in the maritime domain. The V-BoW feature representation is analogous to the bag of words" (BoW) representation used in information retrieval (IR) application in text or natural language processing (NLP) domain. It has been shown in the textual IR applications that the performance of the BoW feature representation can be improved significantly by applying appropriate term-weighting such as log term frequency, inverse document frequency etc. Given the close correspondence between textual BoW (T-BoW) and V-BoW feature representations, we propose to apply several well-known term weighting schemes from the text IR domain on V-BoW feature representation to increase its ability to discriminate between ship types.

Citation Download Citation

Shibin Parameswaran and Katie Rainey "Vessel classification in overhead satellite imagery using weighted "bag of visual words"", Proc. SPIE 9476, Automatic Target Recognition XXV, 947609 (22 May 2015); https://doi.org/10.1117/12.2177779

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