A New Benchmark and an Attribute-Guided Multilevel Feature Representation Network for Fine-Grained Ship Classification in Optical Remote Sensing Images

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 1271 - 1285
• Main Authors: Zhang, Xiaohan, Lv, Yafei, Yao, Libo, Xiong, Wei, Fu, Chunlong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Attribute information; Benchmark testing; Benchmarks; Classification; Feature extraction; fine-grained classification; Image classification; Information processing; Marine vehicles; Military applications; Multilevel; multilevel features; Optical imaging; optical remote sensing image; Remote sensing; Representations; ship classification; Ships; Task analysis; Visual perception; Visualization
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.2981686

Maritime activities are essential aspects of human society. Accurate classification of ships is vital for maritime surveillance and meaningful to numerous civil and military applications. However, most studies conducted are limited to the coarse-grained ship classification. Few studies on fine-grained ship classification have been undertaken despite its accuracy and practicability. In this study, we construct a new benchmark for fine-grained ship classification which consists of 23 fine-grained categories of ships. Besides the category label, the benchmark contains several other attribute information. To solve the problem of interclass similarity, an attribute-guided multilevel enhanced feature representation network (AMEFRN) is proposed. Concretely, a multilevel enhanced visual feature representation is designed to fuse the reweighted regional features in order to focus more on the silent region and suppress the other regions. Further to this, considering the complementary role of attribute information in ship identification, an attribute-guided feature extraction branch is proposed, which extracts the auxiliary attribute features by utilizing the attribute information as supervision. Finally, the attribute features and the enhanced visual features jointly function as a feature representation for classification. Compared to other existing classification models, AMEFRN has better performance with an overall accuracy rate of 93.58% on the established fine-grained ship classification dataset. Moreover, it can be easily embedded into most CNN models as well as trained end-to-end.