Abstract
The hyperspectral image classification method based on recurrent neural network (RNN) regards the spectral values of all bands of each pixel as spectral sequences. But a one-way RNN can only focus on current input and past memory states, not future memories. And RNN itself has the problem of severe gradient vanish. In this paper, bidirectional gated recurrent units (BiGRU) is used for the classification of hyperspectral images. Bi-directional can not only integrate past memory state and future memory state, but also solve the gradient punishment problem of RNN to a certain extent. And the proposed method obtains better classification performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
