Abstract
This paper proposes a two dimensional quaternion valued singular spectrum analysis based method for enhancing the hyperspectral image. Here, the enhancement is for performing the object recognition, but neither for improving the visual quality nor suppressing the artifacts. In particular, the two dimensional quaternion valued singular spectrum analysis components are selected in such a way that the ratio of the interclass separation to the intraclass separation of the pixel vectors is maximized. Next, the support vector machine is employed for performing the object recognition. Compared to the conventional two dimensional real valued singular spectrum analysis based method where only the pixels in a color plane is exploited, the two dimensional quaternion valued singular spectrum analysis based method fuses four color planes together for performing the enhancement. Hence, both the spatial information among the pixels in the same color plane and the spectral information among various color planes are exploited. The computer numerical simulation results show that the overall classification accuracy based on our proposed method is higher than the two dimensional real valued singular spectrum analysis based method, the three dimensional singular spectrum analysis based method, the multivariate two dimensional singular spectrum analysis based method, the median filtering based method, the principal component analysis based method, the Tucker decomposition based method and the hybrid spectral convolutional neural network (hybrid SN) based method.
Highlights
Introduction published maps and institutional affilWith the development of the terrain survey, recognizing different objects on the wide ground becomes more and more important for the agriculture monitoring and the forestry management [1]
The two dimensional quaternion valued singular spectrum analysis components are selected in such a way that the ratio of the interclass separation to the intraclass separation of the pixel vectors of the corresponding reconstructed image in the training set is maximized
The two dimensional quaternion valued singular spectrum analysis components are selected such that the ratio of the interclass separation to the intraclass separation of the pixel vectors is maximized
Summary
Since the quaternion valued signals consist of four components, the two dimensional quaternion valued singular spectrum analysis is effective for fusing the multichannel two dimensional signals together. The procedures for performing the two dimensional quaternion valued singular spectrum analysis are reviewed as follows: 2.1.1. Let H a×b be the set of the a × b quaternion valued matrices. Let x be a quaternion valued matrix with the size equal to h × w and Zs,t be the element in the sth row and the tth column of x. Let the size of the window be u × v. Similar to performing the embedding operation in the two dimensional real valued singular spectrum analysis, the window is moved from top to bottom and from left to right. The quaternion valued trajectory matrix is constructed as follows:
Sign-in/Register to view highlights & summary 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.
