Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 13, Issue 11, Pages 1666-1670Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2016.2601930
Keywords
Change detection; change vector analysis (CVA); cosine angle distance (CAD); deep belief networks (DBNs); multi-spectral images
Categories
Funding
- National Natural Science Foundation of China [61273317, 61422209]
- National Program for the Support of Top-Notch Young Professionals of China
- Specialized Research Fund for the Doctoral Program of Higher Education [20130203110011]
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Due to the noise interference and redundancy in multispectral images, it is promising to transform the available spectral channels into a suitable feature space for relieving noise and reducing the redundancy. The booming of deep learning provides a flexible tool to learn abstract and invariant features directly from the data in their raw forms. In this letter, we propose an unsupervised change detection technique for multispectral images, in which we combine deep belief networks (DBNs) and feature change analysis to highlight changes. First, a DBN is established to capture the key information for discrimination and suppress the irrelevant variations. Second, we map bitemporal change feature into a 2-D polar domain to characterize the change information. Finally, an unsupervised clustering algorithm is adopted to distinguish the changed and unchanged pixels, and then, the changed types can be identified by classifying the changed pixels into several classes according to the directions of feature changes. The experimental results demonstrate the effectiveness and robustness of the proposed method.
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