Comparing Machine Learning and Classical Approaches for Detection of Camouflage Targets in Hyperspectral Data

This study compares two machine learning pixel classifiers with classical approaches for detecting camouflage targets in hyperspectral data. Recent applications of machine learning for hyperspectral data exploitation show good land cover classification results. However, the spectral differences between the classes in those studies are usually very high. We evaluate their performance for classifying targets with similar spectra, specifically camouflage objects. The machine learning results are compared to the established ACE and SVM multiclass classifiers. Input parameters for all approaches, such as training data, spectral class references, and background information, are extracted from the same label set in a single flight line. The evaluation is carried out on 15 different datasets of the same area. We evaluate the results on hyperspectral data from an elaborate measurement campaign using a drone-borne HySpex Mjolnir VS-620 using the combined VNIR and SWIR information. The results show that the SVM produces the best overall accuracy in this experiment with highly unbalanced classes. The machine learning approaches PGBS-HSI and SpectralFormer show better results for the classes with fewer samples. The ACE has the best average but lowest overall accuracy among the tested methods. The findings of this study contribute to understanding the strengths and limitations of machine learning and classical approaches for camouflage target detection in hyperspectral data.