Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In order to alleviate the subsequent computation burden and storage requirement, band selection has been widely adopted to reduce the dimensionality of hyperspectral images, and the current methods mainly consist of the supervised and the unsupervised. Although these supervised methods have better performance, those unsupervised methods dominate the band selection field. In this letter, based on the unique properties of hyperspectral images,  propose a very simple but effective supervised band selection algorithm based on the local spatial information of the hyperspectral image and wrapper method. By using both the information of labeled and unlabeled pixels of the hyperspectral image, our proposed algorithm consistently outperforms the classical wrapper method.  use five widely used real hyperspectral data to demonstrate the effectiveness of our proposed algorithms.  also analyze the relationship beten our band selection algorithm and the ll-known Markov random field classifier.
['Xianghai Cao', 'Tao Xiong', 'Licheng Jiao']
I am writing a research paper on a idea Supervised Band Selection Using Local Spatial Information for Hyperspectral Image
[{"from": "human", "value": "I am writing a research paper on a idea Supervised Band Selection Using Local Spatial Information for Hyperspectral Image"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In order to alleviate the subsequent computation burden and storage requirement, band selection has been widely adopted to reduce the dimensionality of hyperspectral images, and the current methods mainly consist of the supervised and the unsupervised. Although these supervised methods have better performance, those unsupervised methods dominate the band selection field. In this letter, based on the unique properties of hyperspectral images,  propose a very simple but effective supervised band selection algorithm based on the local spatial information of the hyperspectral image and wrapper method. By using both the information of labeled and unlabeled pixels of the hyperspectral image, our proposed algorithm consistently outperforms the classical wrapper method.  use five widely used real hyperspectral data to demonstrate the effectiveness of our proposed algorithms.  also analyze the relationship beten our band selection algorithm and the ll-known Markov random field classifier."}]