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 [1]. 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. However, new challenges arise in dealing with extremely large data sets. For a hypercube with relatively small spatial dimension of 600 ? 400 pixels at 16 bits-per-band-per-pixel, the data volume becomes 120 MB for 250 spectral bands. In some cases, this large data volume can be linearly increased when multiple hypercubes are acquired across time to monitor system dynamics in consecutive time instants. When the ratio between the feature dimension (spectral bands) and the number of data samples (in vector-based pixels) is vastly different, high-dimensional data suffers from the well-known curse of dimensionality. For feature extraction and dimensionality reduction, principal components analysis (PCA) is widely used in HSI [2], where the number of extracted components is significantly reduced compared to the original feature dimension, i.e., the number of spectral bands. For effective analysis of large-scale data in HSI, conventional PCA faces three main challenges: