Application of FABEMD to hyper-spectral image classification
碩士 === 國立中興大學 === 土木工程學系所 === 101 === Hyperspectral images provide a great number of spectral information and have been broadly applied to image classifications. However, the scattered pixel problem due to atmosphere noises and incomplete classification leading unsatisfactory classification accuracy...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2013
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| Online Access: | http://ndltd.ncl.edu.tw/handle/43229087812306432050 |
| Summary: | 碩士 === 國立中興大學 === 土木工程學系所 === 101 === Hyperspectral images provide a great number of spectral information and have been broadly applied to image classifications. However, the scattered pixel problem due to atmosphere noises and incomplete classification leading unsatisfactory classification accuracy remains to be solved. A denoising process includes noise detection and deletion. This paper integrates Fast and Adaptive Bi-dimensional Emperical Mode Decomposition (FABEMD) and Minimum Noise Fraction (MNF) as a two-step denoising process to improve classification accuracy on a hyperspectral image. Regarded as low pass filter, FABEMD decomposes a hyperspectral image into several Bi-dimensional Intrinsic Mode Functions (BIMFs) and a residue image. Some of BIMF are integrated through image fusion to extracted informative images which is subsequently classified through a SVM classifier. The proposed two-step denoising process was tested on AVIRIS Indian Pines hyperspectral image and enhanced the overall accuracy up to 98.14% on the 16-classes classification. The result obtains a significant improvement in hyperspectral classification accuracy compared to the traditional and MNF-based SVMs. The proposed two-step denoising process combining FABEMD with MNF was proven to effectively eliminate a noise effect on hyperspectral images.
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