An improved InceptionV3 network for obscured ship classification in remote sensing images

Kun Liu, Shengtao Yu, Sidong Liu

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
16 Downloads (Pure)


Ship target classification plays an important role in tasks such as maritime traffic control, maritime target tracking, and military reconnaissance. The complex ocean environment often causes obscuration of the ship targets, thus resulting in low accuracy of the obscured targets. This article presents a novel target classification algorithm—improved InceptionV3 and center loss convolution neural network (IICL-CNN)—based on the well-established inception network to improve the accuracy of obscured targets. This algorithm features a new objective function, which is designed to learn common features of both the clear samples and the obscured samples and, in the meantime, reduce the intraclass distance among the obscured samples. Experiments were performed on an optical remote sensing image dataset which consisted of 48 000 ship images in nine categories. The proposed method demonstrated superior performance on the obscured ship targets compared to the original InceptionV3 model. On average, the accuracy was 4.23%, 5.98%, and 17.48% higher on the ship targets that were occluded by levels of 30%, 50%, and 70%, respectively. Our experimental results showed that the proposed IICL-CNN could effectively improve the accuracy of the ship targets at various occlusion levels.
Original languageEnglish
Pages (from-to)4738 - 4747
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication statusPublished - 2020

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Convolution neural network (CNN)
  • deep learning
  • feature extraction
  • fog occlusion
  • image classification
  • remote sensing image


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