Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The classification of very high resolution panchromatic images from urban areas is addressed. The spectral information, i.e. the gray level of each pixel, does generally not ensure a reliable classification. ,  investigate the use of an area filter to extract information about the inter-pixel dependency. The classification is then performed using a support vector machines (SVM) classifier. Using a linear composition of kernels,  define a kernel using both the spectral (original gray level) and the spatial information. A ighting parameter, controlling the relative importance of each feature, is introduced and tuned during the SVM's training process. Experiments have been conducted on simulated panchromatic Pleiades data over Toulouse, France. Results obtained with the proposed approach is positively compared to those obtained with the standard use of gray value information only and classical SVM formulation.
['Mathieu Fauvel', 'Jocelyn Chanussot', 'Jon Atli Benediktsson']
I am writing a research paper on a idea A joint spatial and spectral SVM’s classification of panchromatic images
[{"from": "human", "value": "I am writing a research paper on a idea A joint spatial and spectral SVM\u2019s classification of panchromatic images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The classification of very high resolution panchromatic images from urban areas is addressed. The spectral information, i.e. the gray level of each pixel, does generally not ensure a reliable classification. ,  investigate the use of an area filter to extract information about the inter-pixel dependency. The classification is then performed using a support vector machines (SVM) classifier. Using a linear composition of kernels,  define a kernel using both the spectral (original gray level) and the spatial information. A ighting parameter, controlling the relative importance of each feature, is introduced and tuned during the SVM's training process. Experiments have been conducted on simulated panchromatic Pleiades data over Toulouse, France. Results obtained with the proposed approach is positively compared to those obtained with the standard use of gray value information only and classical SVM formulation."}]