Target Class Oriented Subspace Detection for Hyperspectral Image Classification by Using Mutual Information and Cross Cumulative Residual Entropy

Hyperspectral image classification has become a very important area of research in recent years. However the classif cation become very challenging as the input dimension is very high. This is because the cost increases with the number of input features used to describe the pixel vectors. This problem can be addressed by reducing the irrelevant features for the task of classification. Moreover all the features are no equally important for classification. Principal Component Analysis (PCA) extracts feature however it depends on global variance. To overcome these limitations, a class-oriented subspace detection method is proposed which measures the relevancy of the selected subspace using Normalized Mutual Information (NMI) and Cross Cumulative Residual Entropy (CCRE). The application of NMI and CCRE over PCA images maximize the relevance as well as provided an uncorrelated subspace. Experimental analysis was performed to assess the effectiveness of the proposed method and the selected subspace was evaluated using the Support Vector Machine (SVM). Our proposed method achieves 87.61% and 88.57% classification accuracy respectively.