Analysis of long-wave Infrared hyperspectral classification performance across changing scene illumination

Abstract

Hyperspectral sensors collect data across a wide range of the electromagnetic spectrum, encoding information about the materials comprising each pixel in the scene as well as atmospheric effects and illumination conditions. Changes in scene illumination and atmospheric conditions can strongly affect the observed spectra. In the long- wave infrared, temperature variations resulting from illumination changes produce widely varying at-aperture signals and create a complex material identification problem. Machine learning techniques can use the high- dimensional spectral data to classify a diverse set of materials with high accuracy. In this study, classification techniques are investigated for a long-wave hyperspectral imager. A scene consisting of 9 different materials is imaged over an entire day providing diversity in scene illumination and surface temperatures. A Support Vector Machine classifier, feedforward neural network, and one-dimensional convolutional neural network (1D-CNN) are compared to determine which method is most robust to changes in scene illumination. The 1D-CNN outperforms the other classification methods by a wide margin when presented hyperspectral data cubes significantly different from the training data distribution. This analysis simulates real-world classifier use and validates the robustness of the 1D-CNN to changing illumination and material temperatures.