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| Titel |
Change Detection Method with Spatial and Spectral Information from Deep Learning |
| VerfasserIn |
Haobo Lyu, Hui Lu |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250147480
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| Publikation (Nr.) |
 EGU/EGU2017-11648.pdf |
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| Zusammenfassung |
| Change detection is a key application of remote sensing technology. For multi-spectral
images, the available spatial information and useful spectral information is both helpful for
data analysis, especially change detection tasks. However, it is difficult that how to
learn the changed features from spatial and spectral information meantime in one
model. In this paper, we proposed a new method which combines 2-dimensional
Convolutional Neural Network and 1-dimensional Recurrent Neural Network for
learn changed feature. Compared with only using spectral information, the spatial
information will be helpful to overcome temporal spectral variance issues. Our method
extracts the spatial difference and spectral difference meantime, and these change
information will be balanced in final memory cell of our model, and the leaned change
information will be exploited to character change features for change detection. Finally,
experiments are performed on two multi-temporal datasets, and the results show
superior performance on detecting changes with spatial information and spectral
information.
Index Terms— Change detection, multi-temporal images, recurrent neural network,
convolutional neural network , deep learning, spatial information, spectral information |
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