Open Access

ARTICLE

Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

by Guoqing Zhou, Liang Huang, Qiao Sun^*

College of Electronic Engineering, Naval University of Engineering, Wuhan, 430033, China

* Corresponding Author: Qiao Sun. Email:

(This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)

Computers, Materials & Continua 2023, 77(2), 1985-2007. https://doi.org/10.32604/cmc.2023.040902

Received 03 April 2023; Accepted 15 September 2023; Issue published 29 November 2023

Abstract

The remote sensing ships’ fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For the complex background of remote sensing images, the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets. For the problem of multi-grained feature mixing in remote sensing images, the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features. The parallel network has different depths, considering both high-level and low-level semantic information. The model achieves better classification performance in remote sensing ship images with multi-granularity mixing. Finally, the model achieves 88.73% and 94.56% accuracy on the public fine-grained ship collection-23 (FGSC-23) and FGSCR-42 datasets, respectively, while the parameter size is only 53.47 M, the floating point operations is 9.9 G. The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.

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