面向高光谱遥感图像的MMRI-Boruta特征选择算法OA

The objective of hyperspectral remote sensing image feature selection was to choose the optimal subset of spectral features from a high-dimensional set,thereby eliminating redundancy and enhancing the efficiency and accuracy of image analysis.A hybrid feature selection algorithm named MMRI-Boruta was proposed.The filter-based MRI feature selection algorithm was initially enhanced by incorporating a new feature importance evaluation metric for MMRI-Boruta.Subsequently,the wrapper-based Boruta algo-rithm was employed to further optimize the feature subset.The strengths of both filter and wrapper algo-rithms were combined for the proposed feature selection algorithm,making it easier to obtain the optimal feature subset.To verify the effectiveness,two classical hyperspectral remote sensing image datasets,In-dian Pines and Salinas,were used for testing.Experimental results demonstrated that the proposed algo-rithm outperformed the comparison algorithms.