Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images.

Area of Science:

• Remote Sensing
• Computer Vision
• Oceanography

Background:

• Ocean ship detection using optical remote sensing is hindered by factors like strong waves, cloud cover, and the challenge of detecting small vessels in large areas.
• Existing methods struggle to balance real-time processing needs with high detection accuracy in complex marine environments.

Purpose of the Study:

• To propose a novel ship detection framework that balances real-time processing with high accuracy for ocean optical remote sensing images.
• To address the limitations of current methods in detecting ships under adverse conditions such as waves, clouds, and complex backgrounds.

Main Methods:

• A locally oriented scene complexity analysis framework is proposed, dividing images into simple and complex local scenes.
• Simple scenes utilize a fast saliency model (FSM) for rapid candidate extraction via pulse response analysis in the frequency domain.
• Complex scenes employ a ship feature clustering model (SFCM) for refined detection against severe background interferences.

Main Results:

• The proposed framework demonstrates superior performance compared to state-of-the-art methods on SPOT-5 and GF-2 datasets.
• It successfully addresses real-time ocean ship detection challenges posed by strong waves, broken clouds, extensive cloud cover, and ship fleet interferences.
• The framework's effectiveness was validated through extensive experiments and demonstrated on onboard processing hardware.

Conclusions:

• The novel ship detection framework effectively enhances detection accuracy and processing speed in challenging oceanographic conditions.
• The scene complexity analysis approach allows for adaptive and efficient ship detection, outperforming existing techniques.
• The method shows significant potential for practical applications in maritime surveillance and monitoring using onboard processing.