Effective subspace detection based on the measurement of both the spectral and spatial information for hyperspectral image classification

Abstract

Subspace detection from high dimensional hyperspectral image (HSI) data cube has become an important area of research for efficient identification of ground objects. Standard feature extraction method such as Principal Component Analysis (PCA) has some drawbacks as it depends solely on global variance of the dataset generated. Folded-PCA (FPCA), an improvement of PCA, offers more benefits over PCA as it envisages both local and global structures of image contents and requires less computation and memory. These superior properties make FPCA more effective for feature extraction in high dimensional remote sensing images e.g. HSIs. Therefore, the proposed feature reduction method combines FPCA feature extraction with Normalized Cross Cumulative Residual Entropy (NCCRE) feature selection, termed as FPCA-NCCRE, for efficient features’ subspace detection. NCCRE is utilised as a means of feature selection over the new features generated from FPCA to obtain a more informative subspace. It is experimented on a real mixed agricultural and an urban hyperspectral dataset. Finally, Kernel Support Vector Machine (KSVM) is implemented to calculate the classification accuracy using the detected subspace. From the experiments, it is observed that the proposed method outperforms the baseline approaches and obtains the highest accuracy of 97.67 and 98.57% on the two real hyperspectral images.