Sea ice change detection from synthetic aperture radar images
based on distinctive analysis and LSTM
Submitted to Remote Sensing Letters
Feng Gao, Xiao Wang, Junyu Dong, Shengke Wang
Last modified: 2018/1/15
Polar sea ice change detection is important for navigation safety and has drawn
great attentions in recent years. In this letter, distinctive analysis and long shortterm memory (LSTM) are integrated to solve problem of the sea ice change detection
for synthetic aperture radar (SAR) images. Firstly, the distinctive analysis algorithm
is utilized to refined the difference image generated by the log-ratio operator. Then,
from the refined difference image, reliable samples are selected to train the LSTM
network. The LSTM network is driven to learn the change information, and a powerful model is built to distinguish changed pixels from noisy background polluted
by the speckle noise. Finally, pixels from the original SAR images are classified into
changed or unchanged class by the LSTM network, and the final change map can
be obtained. Experimental results on two real SAR datasets demonstrate the effectiveness and superiority of the proposed method over three closely related methods.
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Links to most related works
- Achanta et al., 2012. "SLIC superpixels compared to state-of-the-art superpixel methods." IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11):
- Celik, T., 2009. "Unsupervised change detection in satellite images using principal
component analysis and k-means clustering." IEEE Geoscience and Remote Sensing
Letters 6 (4): 772-776.
- Gao, F., Dong, J., Li, B., Xu, Q., Xie, C., 2016. "Change detection from synthetic
aperture radar images based on neighborhood-based ratio and extreme learning
machine." Journal of Applied Remote Sensing 10 (4): 046019.
- Gao, F., Dong, J., Li, B., Xu, Q., 2016. "Automatic change detection in synthetic
aperture radar images based on PCANet." IEEE Geoscience and Remote Sensing
Letters 13 (12): 1792-1796.
- Gong, M., Jia, M., Su, L., Wang, S., Jian, L., 2014. "Detecting changes of the Yellow
River Estuary via SAR images based on local fit-search model and kernel-induced
Graph Cuts." International Journal of Remote Sensing 35 (11): 4009-4030.
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