relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3449/
title: Improving the Bag-of-Words model with Spatial Pyramid matching using data augmentation for fine-grained arbitrary-oriented ship classification
creator: Luu, Viet Hung
creator: Dinh, Van Kiet
creator: Luong, Nguyen Hoang Hoa
creator: Bui, Quang Hung
creator: Nguyen, Thi Nhat Thanh
subject: Information Technology (IT)
subject: Scopus-indexed journals
subject: ISI-indexed journals
description: In this letter, we investigate fine-grained classification of arbitrary-oriented ships in very high resolution optical imagery using Bag of Word model with Spatial Pyramid (SP-BoW). Given that based on ‘spatial pyramid’ of the histogram of local features, the final feature vectors not only count the multiplicity of ‘words’ but also represent their spatial topology. We attempt to improve the performance of this model by introducing augmented data for training phase. Our aim is to make the dataset big enough to be able to capture holistic variation of ship orientation. Three data augmentation operations are used including random rotate by an angle of modulo 90°, random flip-left-right, and random flip-top-bottom. Through this procedure, our trained SP-BoW model is able to get better generalization. The proposed approach is validated on the High-Resolution Ship Collections 2016 (HRSC2016) ship dataset. The results indicate that training on augmented data can significantly improve the performance of SP-BoW. Beside, compared to other state-of-the-art convolutional neural network-based approaches, the approach proposed in this research has yielded competitive results and could make it a good baseline for evaluating more sophisticated CNN architecture in the future.
publisher: Taylor & Francis
date: 2019
type: Article
type: PeerReviewed
identifier:   Luu, Viet Hung and Dinh, Van Kiet and Luong, Nguyen Hoang Hoa and Bui, Quang Hung and Nguyen, Thi Nhat Thanh  (2019) Improving the Bag-of-Words model with Spatial Pyramid matching using data augmentation for fine-grained arbitrary-oriented ship classification.  Remote Sensing Letters, 10  (9).   pp. 826-834.  ISSN 2150-704X