Analysis on Saliency Estimation Methods in High-Resolution Optical Remote Sensing Imagery for Multi-Scale Ship Detection
Ship detection is of considerable significance in both military and civilian application domains. Deep Convolutional Neural Network (DCNN) with region proposal mechanism, e.g., Faster R-CNN, performs outstandingly in ship detection with high-resolution images. However, the accuracy limitation is ind...
Main Authors: | Zezhong Li, Yanan You, Fang Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9237930/ |
Similar Items
-
Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images
by: Jian He, et al.
Published: (2020-04-01) -
A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery
by: Fang Xu, et al.
Published: (2017-03-01) -
Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description
by: Ting Nie, et al.
Published: (2020-01-01) -
Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images
by: Yin Zhuang, et al.
Published: (2018-11-01) -
Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor
by: Chao Dong, et al.
Published: (2018-03-01)