The Airborne Visible/Infrared Imaging Spectrometer-4 (AVIRIS-4) represents the next generation in the series of airborne imaging spectrometers built by NASA JPL. Commissioned by the Swiss ARES research consortium, AVIRIS-4 is geared towards delivering cutting-edge imaging spectroscopy data for scientific and practical applications as a replacement for its predecessor APEX. AVIRIS-4 is based on a Dyson-type imaging spectrometer design, also employed by NASA-operated AVIRIS-3 and EMIT, and integrates a scaled two-mirror telescope housed in a compact vacuum vessel. This enables airborne measurements in unpressurized aircraft at altitudes ranging from 500 m to 7620 m, achieving image resolutions between 0.3 and 4.5 m with a field of view of 40.2° in 1241 spatial pixels. AVIRIS-4 surpasses previous state-of-the-art sensor heads in signal-to-noise ratio performance and features a spectral range of 375 to 2504 nm and 7.4 nm spectral sampling. The operation, data capture and mission control hardware as well as the calibration and data processing software is developed by UZH, EPFL, and ZHAW. This paper outlines the design and calibration strategies implemented in AVIRIS-4’s development and highlights its performance during its first year of operation in 2024.

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.

This work discusses three individual camouflage experiments from a drone-based hyperspectral measurement campaign conducted in 2021. The experiments were designed to provide insight into different scenarios of camouflage classification and detection of camouflaged objects. The first experiment demonstrates an approach to detect different objects under camouflage using spectral unmixing. The second experiment presents the performance of commonly used hyperspectral classifiers for camouflage detection with respect to natural illumination changes throughout the day. Finally, the third experiment evaluates the effect of moisture on camouflage detection. For all experiments, we discuss the conditions under which hyperspectral data together with established detection and classification approaches can be used to robustly locate camouflage nets, and when detection is impaired.

Hyperspectral instruments on UAV systems are increasingly used for extensive and repeated data acquisitions. This paper describes an efficient automatic atmospheric and topographic correction for such instruments. It is based on the DROACOR apparent reflectance outputs for flat terrain, which are further corrected for terrain illumination variations and cast shadows. The correction uses a specific cast shadow detection routine in combination with a semi-empirical estimate of the portion of indirect irradiance on the shaded pixels. Results on three example cases show that ground reflectances can successfully be revealed in shaded areas by the presented method.

HyperThun’22 was a multi-sensor and multi-temporal camouflage detection campaign with drone-carried hyper-spectral, thermal, and RGB instruments. In more than 20 flights, various military targets were imaged with the purpose of analysing detection rates, camouflage transparency, and system performances. This article presents the campaign design, the data processing, and first data insights. Preliminary results show the potential of the acquired data for promising studies.

Hyperspectral target detection experiments under nonideal conditions are scarce. An extensive multi-scale and multi-temporal field experiment was designed towards the goal of knowledge expansion under such circumstances. A range of camouflage materials and specific targets of interest were placed in a realistic natural environment with vegetation cover and varying illumination. In several experiments, aspects like changes in the sun position, variable moisture, and relocations of targets were analysed. Using an aircraft-based and a drone-based imaging spectrometer, the target scenarios were mapped at different daytimes. The data were radiometrically, atmospherically and geometrically processed to allow subsequent data analysis. First insights deliver promising results.