LMO-YOLO: A Ship Detection Model for Low-Resolution Optical Satellite Imagery
Author:
Affiliation:
1. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
2. Image Processing Center, School of Astronautics, Beihang University, Beijing, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09779558.pdf?arnumber=9779558
Reference58 articles.
1. Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature;shi;IEEE Trans Geosci Remote Sens,2013
2. Sparse anchoring guided high-resolution capsule network for geospatial object detection from remote sensing imagery
3. Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images
4. Inshore ship detection in remote sensing images based on deep features
5. S-CNN-based ship detection from high-resolution remote sensing images;zhang;Int Arch Photogramm Remote Sens Spatial Inf Sci,2016
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