Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  concentrates on reducing the computational complexity of hyperspectral image band selection algorithms via one-bit transform which can be obtained using simple filtering and comparison operations. Firstly, one-bit transform of each band is obtained and noisy and less-discriminative bands, which are decided according to the total number of vertical and horizontal transitions in their one-bit representations, are eliminated. Then remained bands are forwarded to the band selection and classification algorithms. Steepest ascents band selection and support vector machine classification are used to demonstrate the performance of the proposed approach. Experimental results show that the proposed approach not only reduces the computational load of the band selection process, but also provides similar or even higher classification accuracy.
['Begum Demir', 'Sarp Erturk']
I am writing a research paper on a idea Reducing the Computational Load of Hyperspectral Band Selection Using the One-Bit Transform of Hyperspectral Bands
[{"from": "human", "value": "I am writing a research paper on a idea Reducing the Computational Load of Hyperspectral Band Selection Using the One-Bit Transform of Hyperspectral Bands"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  concentrates on reducing the computational complexity of hyperspectral image band selection algorithms via one-bit transform which can be obtained using simple filtering and comparison operations. Firstly, one-bit transform of each band is obtained and noisy and less-discriminative bands, which are decided according to the total number of vertical and horizontal transitions in their one-bit representations, are eliminated. Then remained bands are forwarded to the band selection and classification algorithms. Steepest ascents band selection and support vector machine classification are used to demonstrate the performance of the proposed approach. Experimental results show that the proposed approach not only reduces the computational load of the band selection process, but also provides similar or even higher classification accuracy."}]