Abstract
Hyperspectral image (HSI) classification has been an active topic in recent years. Over the past few decades, a significant number of methods have been proposed to deal with this problem. However amongst these methods, deep learning based methods are rare. Inspired by the excellent performance of deep convolutional neural network (DCNN) in visual image classification, in this paper, we introduce DCNN into HSI classification. Instead of using two-dimension kernels as DCNN is used in two-dimension image classification, one-dimension kernels is adopted in our DCNN to fit the HSI context. The proposed method is compared with the state-of-the-art deep learning based HSI classification methods, evaluated on two popular datasets, and produces better classification results.
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.
