Leaf maturation from initiation to senescence is a phenological event of plants that is a result of the influences of temperature and water availability on physiological activities during a life cycle. Detection of newly grown leaves (NGL) is therefore useful in diagnosis if growth of trees, tree stress and even climatic change. There are many important applications that can naturally be modeled as a low-rank plus a sparse contribution. This paper develop a new algorithm and application to detect NGL. It uses first sparse matrix as a preprocessing to enhance target and applied deep learning to segment the image. The experimental results show that our proposed method can detect targets effectively and decrease false alarm rate.
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.