Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a sparse-representation-based classification approach with a novel dictionary construction procedure. By using the constructed dictionary, sophisticated prior knowledge about the spatial nature of the image can be integrated. The approach is based on the assumption that each image patch can be factorized into characteristic spatial patterns, also called shapelets, and patch-specific spectral information. A set of shapelets is learned in an unsupervised way, and spectral information is embodied by training samples. A combination of shapelets and spectral information is represented in an undercomplete spatial–spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch. The patch-based classification is obtained by means of the representation error. Experiments are conducted on three ll-known hyperspectral image data sets. They illustrate that our proposed approach shows superior results in comparison to sparse-representation-based classifiers that use only limited spatial information and behaves competitively with or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse-representation-based classifiers.
['Ribana Roscher', 'Björn Waske']
I am writing a research paper on a idea Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images
[{"from": "human", "value": "I am writing a research paper on a idea Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a sparse-representation-based classification approach with a novel dictionary construction procedure. By using the constructed dictionary, sophisticated prior knowledge about the spatial nature of the image can be integrated. The approach is based on the assumption that each image patch can be factorized into characteristic spatial patterns, also called shapelets, and patch-specific spectral information. A set of shapelets is learned in an unsupervised way, and spectral information is embodied by training samples. A combination of shapelets and spectral information is represented in an undercomplete spatial\u2013spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch. The patch-based classification is obtained by means of the representation error. Experiments are conducted on three ll-known hyperspectral image data sets. They illustrate that our proposed approach shows superior results in comparison to sparse-representation-based classifiers that use only limited spatial information and behaves competitively with or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse-representation-based classifiers."}]