MIPST-14 Compressed-Domain Ship Detection on Space borne Optical Image Using Deep Neural Network and Extreme Learning Machine

Ship detection on space borne 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 space borne 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 a for mentioned 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.

  • Project Category : IEEE Projects
  • Project Year : 2015-2016
  • Department
  • B.E(Computer Science) , B.E(Electrical and Electronics Engg), B.E(Electronics and Communication), B.Tech, M.E(Computer Science), M.E(SOFTWATE ENGG), M.SC(SOFTWARE ENGG), M.Tech,
  • Domain
  • Digital Image Processing, MATLAB Projects,
  • Technology
  • matlab,
  • Avilable city
  • Chennai, Davangere, Pondicherry, Thanjavur,

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