R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 6; p. 631
• Main Authors: Zhang, Shaoming, Wu, Ruize, Xu, Kunyuan, Wang, Jianmei, Sun, Weiwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2019
• Subjects: Algorithms; Artificial neural networks; Deep learning; Deformation effects; Detection; Earth science; False alarms; Feature maps; Formability; GaoFen-2 remote sensing image; gathering ship; High resolution; Image detection; Image resolution; Methods; Neural networks; Pattern recognition; regional convolutional neural network; Remote sensing; Rivers; ship detection; Ships; small ship; Support vector machines; Synthetic aperture radar; Target detection; United States > US; China

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland riv...