Effects of atmospheric, topographic, and BRDF correction on imaging spectroscopy-derived data products

Surface reflectance is an important data product in imaging spectroscopy for obtaining surface information. The complex retrieval of surface reflectance, however, critically relies on accurate knowledge of atmospheric absorption and scattering, and the compensation of these effects. Furthermore, illumination and observation geometry in combination with surface reflectance anisotropy determine dynamics in retrieved surface reflectance not related to surface absorption properties. To the best of authors’ knowledge, no comprehensive assessment of the impact of atmospheric, topographic, and anisotropy effects on derived surface information is available so far.This study systematically evaluates the impact of these effects on reflectance, albedo, and vegetation products. Using three well-established processing schemes (ATCOR F., ATCOR R., and BREFCOR), high-resolution APEX imaging spectroscopy data, covering a large gradient of illumination and observation angles, are brought to several processing states, varyingly affected by mentioned effects. Pixel-wise differences of surface reflectance, albedo, and spectral indices of neighboring flight lines are quantitatively analyzed in their respective overlapping area. We found that compensation of atmospheric effects reveals actual anisotropy-related dynamics in surface reflectance and derived albedo, related to an increase in pixel-wise relative reflectance and albedo differences of more than 40%. Subsequent anisotropy compensation allows us to successfully reduce apparent relative reflectance and albedo differences by up to 20%. In contrast, spectral indices are less affected by atmospheric and anisotropy effects, showing relative differences of 3% to 10% in overlapping regions of flight lines.We recommend to base decisions on the use of appropriate processing schemes on individual use cases considering envisioned data products

A multi-temporal hyperspectral camouflage detection and transparency experiment

Hyperspectral sensors are used to measure the electromagnetic spectrum in hundreds of narrow and contiguous spectral bands. The recorded data exhibits characteristic features of materials and objects. For tasks within the security and defense domain, this valuable information can be gathered remotely using drones, airplanes or satellites. In 2021, we conducted an experiment in Ettlingen, Germany, using a drone-borne hyperspectral sensor to record data of various camouflage setups. The goal was the inference of camouflage detection limits from typical hyperspectral data evaluation approaches for different scenarios. The experimental site is a natural strip of vegetation between two corn fields. Our main experiment was a camouflage garage that covered different target materials and objects. The distance between the targets and the roof of the camouflage garage was modified during the experiment. Together with the target variations, this was done to determine the material dependent detection limits and the transparency of the camouflage garage. Another experiment was carried out using two different types of camouflage nets in various states of occlusion by freshly cut vegetation. This manuscript contains a detailed experiment description, as well as, the first results of the camouflage transparency and occlusion experiment. We show that it is possible to determine the target inside the camouflage garage and that vegetation cover is not suitable additional camouflage for hyperspectral sensors

About the transferability of topographic correction methods from spaceborne to airborne optical data

In rugged terrain, topography substantially influences the illumination and observation geometry and thus the bidirectional reflectance distribution function (BRDF) of a surface. While this problem has been known and investigated for spaceborne optical data since the 1980s, it has led to several well-known topographic correction methods. To date, the methods developed for spaceborne data were equivalently applied to airborne data with distinctly higher spatial resolution, illumination/observation angle configurations and finally (instantaneous) field of view ((i)FOV). On the one hand, this paper evaluates, whether such a transfer of methods from spaceborne to airborne acquisitions is reasonable. On the other hand, a new Lambertian/statistical-empirical (LA+SE) correction method is introduced. While in the spaceborne case the modified Minnaert (MM) and the statistical-empirical (SE) methods performed best, MM led to the statistically and visually best compromise for the airborne data. Our results suggest further, that with a higher spatial resolution various effects come into play (FOV widening; changing the fraction of geometric, volumetric and isotropic scattering, etc.), compromising previously successful methods, such as the statistical-empirical (SE) method

A multi-scale and multi-temporal hyperspectral target detection experiment - from design to first results

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

About the Transferability of Topographic Correction Methods From Spaceborne to Airborne Optical Data

In rugged terrain, topography substantially influences the illumination and observation geometry, and thus, the bidirectional reflectance distribution function (BRDF) of a surface. While this problem has been known and investigated for spaceborne optical data since the 1980s, it has led to several well-known topographic correction methods. To date, the methods developed for spaceborne data were equivalently applied to airborne data with distinctly higher spatial resolution, illumination/observation angle configurations and finally (instantaneous) field of view (FOV). On the one hand, this article evaluates, whether such a transfer of methods from spaceborne to airborne acquisitions is reasonable. On the other hand, a new Lambertian/statistical-empirical correction method is introduced. While in the spaceborne case the Modified Minnaert (MM) and the Statistical-Empirical (SE) methods performed best, MM led to the statistically and visually best compromise for the airborne data. Our results suggest further that with a higher spatial resolution various effects come into play (FOV widening; changing the fraction of geometric, volumetric and isotropic scattering, etc.), compromising previously successful methods, such as the SE method

A multi-temporal hyperspectral target detection experiment: evaluation of military setups

This paper shows three experiments from our HyperGreding’19 campaign that combine multi-temporal hyper�spectral data to address several essential questions in target detection. The experiments were conducted over Greding, Germany, using a Headwall VNIR/SWIR co-aligned sensor mounted on a drone with a flight altitude of 80 m. Additionally, high-resolution aerial RGB data, GPS measurements, and reference data from a field spectrometer were recorded to support the hyperspectral data pre-processing and the evaluation process for the individual experiments. The focus of the experiments is the detectability of camouflage materials and camou�flaged objects. When the goal is to transfer hyperspectral analysis to a practical setting, the analysis must be robust regarding realistic and changing conditions. The first experiment investigates the SAM and the SAMZID approaches for change detection to demonstrate their usefulness for target detection of moving objects within the recorded scene. The goal is to eliminate unwanted changes like shadow areas. The second experiment eval�uates the detection of different camouflage net types over two days. This includes camouflage nets in shadows during one flight and brightly illuminated in another due to varying solar elevation angles during the day. We demonstrate the performance of typical hyperspectral target detection and classification approaches for robust detection under these conditions. Finally, the third experiment aims to detect objects and materials behind the cover of camouflage nets by using a camouflage garage. We show that some materials can be detected using an unmixing approach

Experimental approach to camouflaged target detection and camouflage evaluation

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

Hyperthun’22: A multi-sensor multi-temporal camouflage detection campaign

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 trans- parency, 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