A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images

With the rapid development of various technologies of satellite sensor, synthetic aperture radar (SAR) image has been an import source of data in the application of change detection. In this paper, a novel method based on a convolutional neural network (CNN) for SAR image change detection is propose...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 57; číslo 8; s. 5751 - 5763
Hlavní autoři: Li, Yangyang, Peng, Cheng, Chen, Yanqiao, Jiao, Licheng, Zhou, Linhao, Shang, Ronghua
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial neural networks
Change detection
Change detection algorithms
Classification
Clustering
Clustering algorithms
Computer simulation
convolutional neural network (CNN)
Deep learning
Detection
Image classification
Image detection
Labels
Neural networks
Radar
Radar detection
Radar imaging
Radar polarimetry
Robustness
SAR (radar)
Satellite imagery
spatial fuzzy clustering
Speckle
Synthetic aperture radar
synthetic aperture radar (SAR) images
Theoretical analysis
Training
ISSN:0196-2892, 1558-0644
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Popis
Shrnutí:With the rapid development of various technologies of satellite sensor, synthetic aperture radar (SAR) image has been an import source of data in the application of change detection. In this paper, a novel method based on a convolutional neural network (CNN) for SAR image change detection is proposed. The main idea of our method is to generate the classification results directly from the original two SAR images through a CNN without any preprocessing operations, which also eliminate the process of generating the difference image (DI), thus reducing the influence of the DI on the final classification result. In CNN, the spatial characteristics of the raw image can be extracted and captured by automatic learning and the results with stronger robustness can be obtained. The basic idea of the proposed method includes three steps: it first produces false labels through unsupervised spatial fuzzy clustering. Then we train the CNN through proper samples that are selected from the samples with false labels. Finally, the final detection results are obtained by the trained convolutional network. Although training the convolutional network is a supervised learning fashion, the whole process of the algorithm is an unsupervised process without priori knowledge. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of our algorithm in simulated and real data sets. In addition, we try to apply our algorithm to the change detection of heterogeneous images, which also achieves satisfactory results.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2019.2901945