Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes an artificial neural network based system for classifying the contents of hyperspectral images that is able to automatically reduce the dimensionality of the data provided by the hyperspectrometers without compromising their efficacy. The data reduction is achieved through the adaptation of the window size and the number of parameters that make up the description of the spectral signatures within the window as training progresses. Following this approach, a user just needs to specify the minimum resolution desired on the output or category image and the level of discrimination among categories, and the system will try to meet these requirements by modifying during training the size and number of inputs to the network. When it is not possible to comply with both requirements, the system will provide a compromise solution that minimizes the global discrimination error, which takes into account the spatial discrimination and the discrimination among classes.
['Alberto Prieto', 'Daniel Souto', 'Richard J. Duro', 'Fernando López-Peña']
I am writing a research paper on a idea An ANN based automatic hyperspectral image processing system with adaptive dimensionality reduction
[{"from": "human", "value": "I am writing a research paper on a idea An ANN based automatic hyperspectral image processing system with adaptive dimensionality reduction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes an artificial neural network based system for classifying the contents of hyperspectral images that is able to automatically reduce the dimensionality of the data provided by the hyperspectrometers without compromising their efficacy. The data reduction is achieved through the adaptation of the window size and the number of parameters that make up the description of the spectral signatures within the window as training progresses. Following this approach, a user just needs to specify the minimum resolution desired on the output or category image and the level of discrimination among categories, and the system will try to meet these requirements by modifying during training the size and number of inputs to the network. When it is not possible to comply with both requirements, the system will provide a compromise solution that minimizes the global discrimination error, which takes into account the spatial discrimination and the discrimination among classes."}]