Abstract
In this paper, we propose a fast 3-D empirical mode decomposition (fTEMD) method for hyperspectral images (HSIs) to achieve class-oriented multitask learning (cMTL). The major steps of the proposed method are twofold: 1) fTEMD and 2) cMTL. On the one hand, the traditional empirical mode decomposition is extended to its 3-D version, which naturally treats the HSI as a cube and effectively decomposes the HSI into several 3-D intrinsic mode functions (TIMFs). To accelerate the fTEMD, 3-D Delaunay triangulation is adopted to determine the distances of extrema, whereas separable filters are implemented to generate the envelopes. On the other hand, cMTL is performed on the TIMFs by taking those TIMFs as features of different tasks. The proposed cMTL learns the representation coefficients by taking advantage of the class labels and fully exploiting the information contained in each TIMF. Experiments conducted on three benchmark data sets demonstrate the effectiveness of the proposed method.
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 callMore From: IEEE Transactions on Geoscience and Remote Sensing
Disclaimer: 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.
