Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine

TLDR

Ship detection in spaceborne optical imagery is challenged by weather sensitivity, large data volumes, and the difficulty of balancing accuracy with algorithmic complexity. The authors propose a method that addresses these issues by extracting wavelet coefficients from the JPEG2000 compressed domain and applying a deep neural network together with an extreme learning machine. Compressed‑domain processing is used for rapid candidate extraction, a DNN supplies high‑level feature representation and classification, and an ELM performs efficient feature pooling and decision making. Experiments show the approach achieves faster detection and higher accuracy than existing state‑of‑the‑art methods.

Abstract

Ship detection on spaceborne images has attracted great interest in the applications of maritime security and traffic control. Optical images stand out from other remote sensing images in object detection due to their higher resolution and more visualized contents. However, most of the popular techniques for ship detection from optical spaceborne images have two shortcomings: 1) Compared with infrared and synthetic aperture radar images, their results are affected by weather conditions, like clouds and ocean waves, and 2) the higher resolution results in larger data volume, which makes processing more difficult. Most of the previous works mainly focus on solving the first problem by improving segmentation or classification with complicated algorithms. These methods face difficulty in efficiently balancing performance and complexity. In this paper, we propose a ship detection approach to solving the aforementioned two issues using wavelet coefficients extracted from JPEG2000 compressed domain combined with deep neural network (DNN) and extreme learning machine (ELM). Compressed domain is adopted for fast ship candidate extraction, DNN is exploited for high-level feature representation and classification, and ELM is used for efficient feature pooling and decision making. Extensive experiments demonstrate that, in comparison with the existing relevant state-of-the-art approaches, the proposed method requires less detection time and achieves higher detection accuracy.