Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Spatial texture features have been demonstrated to be very useful for the  recently-proposed representation-based classifiers, such as the sparse representation-based classifier (SRC) and nearest regularized subspace (NRS). In this work, a ighted  residual-fusion-based strategy with multiple features is proposed for these classifiers. Multiple features include local binary patterns (LBP), Gabor features, and the original spectral signatures. In the proposed classification framework, representation residuals for a testing pixel from using each type of features are ighted to generate the final representation residual, and then the label of the testing pixel is determined according to the class yielding the minimum final residual. The motivation of this work is that different features represent pixels from different perspectives and their fusion in the residual domain can enhance the discriminative ability. Experimental results of several real hyperspectral image datasets demonstrate that the proposed residual-based fusion outperforms the original NRS, SRC, support vector machine (SVM) with LBP, and SVM with Gabor features, even in small-sample-size (SSS) situations.
['Bing Peng', 'Wei Li', 'Xiaoming Xie', 'Qian Du', 'Kui Liu']
I am writing a research paper on a idea Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery
[{"from": "human", "value": "I am writing a research paper on a idea Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Spatial texture features have been demonstrated to be very useful for the  recently-proposed representation-based classifiers, such as the sparse representation-based classifier (SRC) and nearest regularized subspace (NRS). In this work, a ighted  residual-fusion-based strategy with multiple features is proposed for these classifiers. Multiple features include local binary patterns (LBP), Gabor features, and the original spectral signatures. In the proposed classification framework, representation residuals for a testing pixel from using each type of features are ighted to generate the final representation residual, and then the label of the testing pixel is determined according to the class yielding the minimum final residual. The motivation of this work is that different features represent pixels from different perspectives and their fusion in the residual domain can enhance the discriminative ability. Experimental results of several real hyperspectral image datasets demonstrate that the proposed residual-based fusion outperforms the original NRS, SRC, support vector machine (SVM) with LBP, and SVM with Gabor features, even in small-sample-size (SSS) situations."}]