Share Email Print
cover

Proceedings Paper

Fine-grained visual marine vessel classification for coastal surveillance and defense applications
Author(s): Berkan Solmaz; Erhan Gundogdu; Kaan Karaman; Veysel Yücesoy; Aykut Koç
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The need for capabilities of automated visual content analysis has substantially increased due to presence of large number of images captured by surveillance cameras. With a focus on development of practical methods for extracting effective visual data representations, deep neural network based representations have received great attention due to their success in visual categorization of generic images. For fine-grained image categorization, a closely related yet a more challenging research problem compared to generic image categorization due to high visual similarities within subgroups, diverse applications were developed such as classifying images of vehicles, birds, food and plants. Here, we propose the use of deep neural network based representations for categorizing and identifying marine vessels for defense and security applications. First, we gather a large number of marine vessel images via online sources grouping them into four coarse categories; naval, civil, commercial and service vessels. Next, we subgroup naval vessels into fine categories such as corvettes, frigates and submarines. For distinguishing images, we extract state-of-the-art deep visual representations and train support-vector-machines. Furthermore, we fine tune deep representations for marine vessel images. Experiments address two scenarios, classification and verification of naval marine vessels. Classification experiment aims coarse categorization, as well as learning models of fine categories. Verification experiment embroils identification of specific naval vessels by revealing if a pair of images belongs to identical marine vessels by the help of learnt deep representations. Obtaining promising performance, we believe these presented capabilities would be essential components of future coastal and on-board surveillance systems.

Paper Details

Date Published: 5 October 2017
PDF: 7 pages
Proc. SPIE 10434, Electro-Optical Remote Sensing XI, 104340A (5 October 2017); doi: 10.1117/12.2278864
Show Author Affiliations
Berkan Solmaz, ASELSAN A.S. (Turkey)
Erhan Gundogdu, ASELSAN A.S. (Turkey)
Kaan Karaman, ASELSAN A.S. (Turkey)
Veysel Yücesoy, ASELSAN A.S. (Turkey)
Aykut Koç, ASELSAN A.S. (Turkey)


Published in SPIE Proceedings Vol. 10434:
Electro-Optical Remote Sensing XI
Gary Kamerman; Ove Steinvall, Editor(s)

© SPIE. Terms of Use
Back to Top