TY  - JOUR
ID  - SisLab3449
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3449/
IS  - 9
A1  - Luu, Viet Hung
A1  - Dinh, Van Kiet
A1  - Luong, Nguyen Hoang Hoa
A1  - Bui, Quang Hung
A1  - Nguyen, Thi Nhat Thanh
Y1  - 2019///
N2  - 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.
PB  - Taylor & Francis
JF  - Remote Sensing Letters
VL  - 10
SN  - 2150-704X
TI  - Improving the Bag-of-Words model with Spatial Pyramid matching using data augmentation for fine-grained arbitrary-oriented ship classification
SP  - 826
AV  - none
EP  - 834
ER  -