Implications of spectral and spatial features to improve the identification of specific classes

Dimensionality is one of the greatest challenges when deciphering hyperspectral imaging data. Although the multiband nature of the data is beneficial, algorithms are faced with a high computational load and statistical incompatibility due to the insufficient number of training samples. This is a hurdle to downstream applications. The combination of dimensionality and the real-world scenario of mixed pixels makes the identification and classification of imaging data challenging. Here, we address the complications of dimensionality using specific spectral indices from band combinations and spatial indices from texture measures for classification to better identify the classes. We classified spectral and combined spatial–spectral data and calculated measures of accuracy and entropy. A reduction in entropy and an overall accuracy of 80.50% was achieved when using the spectral–spatial input, compared with 65% for the spectral indices alone and 59.50% for the optimally determined principal components.