Since Hyperspectral images (HSIs) contain a large amount of spectral information, they can provide detailed spectral information and enable accurate CD. However, the spectral heterogeneity of HSIs may lead to false alarms which will reduce detection accuracy. Additionally, it is difficult to collect and annotate pixel-level labels for CD in HSIs. Therefore, we propose an unsupervised symmetric tensor network (USTN) for HSIs CD. We design a novel multidimensional symmetric tensor framework to solve the problem of high-dimensional data processing. Furthermore, the framework integrates a spatial edge loss to preserve detailed spectral-spatial information. Finally, we use feature fusion to suppress the invariant components (i.e., the background) and highlight the variant components (i.e., temporal changes). Experiments on two sets of multitemporal HSIs, Hermiston and Bay Area, demonstrate the effectiveness of USTN for binary change detection.
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.