For change detection in multi-source heterogeneous images, this paper proposes a change detection method using a refined hierarchical clustering approach. For multi-source heterogeneous multi-temporal images, Stacked Denoising Autoencoders (SDAE) is used to extract deep features from multi-source heterogeneous images. On this basis, the comparability of heterogeneous data in deep feature space is guaranteed by iterative transformation of features. Finally, the correlation between deep features of heterogeneous data is described by introducing a variety of distance measures, and the hierarchical clustering method is improved. The classification of change types is gradually realized through multiple clustering, while improving the accuracy of change detection.
A new method based on a Network in Network (NIN) structure is proposed to detect target changes from multi-temporal optical remote sensing images. Firstly, the changed areas are captured by a change detection method based on multifeature fusion, and the changed patches are obtained by morphological processing. Then, a convolutional neural network with an NIN structure is constructed to train the target recognition model using a small number of samples and to distinguish the original images corresponding to the tchanged patches. Finally, a recognition strategy combining preliminary screening and thorough screening is designed, and multiple thresholds are assigned according to the patch size to avoid the possible false detection brought by a single threshold. Based on experiments with multi-temporal airport images, the overall accuracy of aircraft target change detection using the method in this study was 91.89%, with a false alarm rate of 10.71%, indicating that this method can accurately and reliably detect target change.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.