Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel hyperspectral image (HSI) classification method by the probabilistic fusion of pixel-level and superpixel-level classifiers is proposed. Generally, pixel-level classifiers based on spectral information only may generate “salt and pepper” result in the classification map since spatial correlation is not considered. By incorporating spatial information in homogeneous regions, the superpixel-level classifiers can effectively eliminate the noisy appearance. Hover, the classification accuracy will be deteriorated if undersegmentation cannot be fully avoided in superpixel-based approaches. Therefore, it is proposed to adaptively combine both the pixel-level and superpixel-level classifiers, to improve the classification performance in both homogenous and structural areas. In the proposed method, a support vector machine classifier is first applied to estimate the pixel-level class probabilities. Then, superpixel-level class probabilities are estimated based on a joint sparse representation. Finally, the two levels of class probabilities are adaptively combined in a maximum  a posteriori  estimation model, and the classification map is obtained by solving the maximum optimization problem. Experimental results on real HSI images demonstrate the superiority of the proposed method over several ll-known classification approaches in terms of classification accuracy.
['Shutao Li', 'Ting Lu', 'Leyuan Fang', 'Xiuping Jia', 'Jon Atli Benediktsson']
I am writing a research paper on a idea Probabilistic Fusion of Pixel-Level and Superpixel-Level Hyperspectral Image Classification
[{"from": "human", "value": "I am writing a research paper on a idea Probabilistic Fusion of Pixel-Level and Superpixel-Level Hyperspectral Image Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel hyperspectral image (HSI) classification method by the probabilistic fusion of pixel-level and superpixel-level classifiers is proposed. Generally, pixel-level classifiers based on spectral information only may generate \u201csalt and pepper\u201d result in the classification map since spatial correlation is not considered. By incorporating spatial information in homogeneous regions, the superpixel-level classifiers can effectively eliminate the noisy appearance. Hover, the classification accuracy will be deteriorated if undersegmentation cannot be fully avoided in superpixel-based approaches. Therefore, it is proposed to adaptively combine both the pixel-level and superpixel-level classifiers, to improve the classification performance in both homogenous and structural areas. In the proposed method, a support vector machine classifier is first applied to estimate the pixel-level class probabilities. Then, superpixel-level class probabilities are estimated based on a joint sparse representation. Finally, the two levels of class probabilities are adaptively combined in a maximum  a posteriori  estimation model, and the classification map is obtained by solving the maximum optimization problem. Experimental results on real HSI images demonstrate the superiority of the proposed method over several ll-known classification approaches in terms of classification accuracy."}]