Class-Specific Random Forest With Cross-Correlation Constraints for Spectral–Spatial Hyperspectral Image Classification

A class-specific random forest (RF) model with cross-correlation constraints is developed for the spectral–spatial hyperspectral image (HSI) classification. The novelties of this letter are as follows: 1) normalization of the spectral feature vector by using cross correlation in the stochastic process and proposal of a spectral–spatial hybrid feature extraction based on the cross-correlation analysis; 2) establishment of an RF classifier model by using class-specific trees (CSTs); and 3) exploration of the performance of the proposed method by comparing with its several traditional classification methods on two real HSI data sets. Further research on the effects of parameter setup, such as the number of CSTs, spectral constraint scale, and size of the spatial neighbor, is discussed in terms of classification accuracy. Experimental results show that the performance of the proposed method is better than that of the traditional methods.