Ship Detection in Complex Environment Using SAR Time Series
Author:
Affiliation:
1. Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai, 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/ielx7/4609443/9656571/09763320.pdf?arnumber=9763320
Reference46 articles.
1. The Rician inverse Gaussian distribution: a new model for non-Rayleigh signal amplitude statistics
2. Change Detection in Image Time-Series Using Unsupervised LSTM
3. MIMOSA: An Automatic Change Detection Method for SAR Time Series
4. Delineation of built-up land change from SAR stack by analysing the coefficient of variation
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