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

Akhil Kallepalli; Anil Kumar; Kourosh Khoshelham; David B. James; Mark A. Richardson

doi:10.1117/1.JRS.13.016504

14 January 2019 Implications of spectral and spatial features to improve the identification of specific classes

Akhil Kallepalli, Anil Kumar, Kourosh Khoshelham, David B. James, Mark A. Richardson

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 016504 (January 2019). https://doi.org/10.1117/1.JRS.13.016504

Abstract

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.

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Akhil Kallepalli, Anil Kumar, Kourosh Khoshelham, David B. James, and Mark A. Richardson "Implications of spectral and spatial features to improve the identification of specific classes," Journal of Applied Remote Sensing 13(1), 016504 (14 January 2019). https://doi.org/10.1117/1.JRS.13.016504

Received: 16 July 2018; Accepted: 14 December 2018; Published: 14 January 2019

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