Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging [Applications Corner]

With numerous and contiguous spectral bands acquired from visible light (400- 1,000 nm) to (near) infrared (1,000-1,700 nm and over), hyperspectral imaging (HSI) can potentially identify different objects by detecting minor changes in temperature, moisture, and chemical content. As a result, HSI has been widely applied in a number of application areas, including remote sensing. HSI data contains two-dimensional (2-D) spatial and one-dimensional spectral information, and naturally forms a three-dimensional (3-D) hypercube with a high spectral resolution in nanometers that enables robust discrimination of ground features. This article discusses several variations and extensions of conventional PCA to address the aforementioned challenges. These variations and extensions include slicing the HSI data for efficient computation of the covariance matrix similarly done in 2-D-PCA analysis and grouping the spectral data to preserve the local structures and further speedup the process to determine the covariance matrix. In addition, we also discuss some non-PCA-based approaches for feature extraction and data reduction, based on techniques such as band selection, random projection, singular value decomposition, and machine-learning approaches such as the support vector machine (SVM).