Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As single-layer feed-forward neural networks, extreme learning machine (ELM) has recently been used with success for the classification of hyperspectral images (HSIs). Hover, the results of pure pixel-wise spectral classifiers often appear very noisy with limited training samples. To further improve the accuracy,  propose a novel spectral-spatial information integrating scheme for pixel-wise kernel ELM-based classifier. In particular,  show that a spatial bilateral filtering information on spectral band-subsets can significantly improve the accuracy of the pixel-wise kernel ELM based classifier. The benefits of the proposed method are twofold: 1) spectral structural similarity guided band-subsets partition and 2) incorporating the spectral-spatial information by bilateral filtering. Experiments on the widely used real HSI demonstrate that the proposed approach outperforms several ll-known classification methods in terms of classification accuracy and low computational cost.
['Yu Shen', 'Jinhuan Xu', 'Heng Li', 'Liang Xiao']
I am writing a research paper on a idea ELM-based spectral-spatial classification of hyperspectral images using bilateral filtering information on spectral band-subsets
[{"from": "human", "value": "I am writing a research paper on a idea ELM-based spectral-spatial classification of hyperspectral images using bilateral filtering information on spectral band-subsets"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As single-layer feed-forward neural networks, extreme learning machine (ELM) has recently been used with success for the classification of hyperspectral images (HSIs). Hover, the results of pure pixel-wise spectral classifiers often appear very noisy with limited training samples. To further improve the accuracy,  propose a novel spectral-spatial information integrating scheme for pixel-wise kernel ELM-based classifier. In particular,  show that a spatial bilateral filtering information on spectral band-subsets can significantly improve the accuracy of the pixel-wise kernel ELM based classifier. The benefits of the proposed method are twofold: 1) spectral structural similarity guided band-subsets partition and 2) incorporating the spectral-spatial information by bilateral filtering. Experiments on the widely used real HSI demonstrate that the proposed approach outperforms several ll-known classification methods in terms of classification accuracy and low computational cost."}]