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.
This paper shows three experiments from our HyperGreding’19 campaign that combine multi-temporal hyperspectral 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 camouflaged 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 evaluates 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.
Hyperspectral remote sensing data can be used for civil and military applications to robustly detect and classify target objects. High spectral resolution of hyperspectral data can compensate for the comparatively low spatial resolution, which allows for detection and classification of small targets, even below image resolution. Hyperspectral data sets are prone to considerable spectral redundancy, affecting and limiting data processing and algorithm performance. As a consequence, data reduction strategies become increasingly important, especially in view of near-real-time data analysis. The goal of this paper is to analyze different strategies for hyperspectral band selection algorithms and their effect on subpixel classification for different target and background materials. Airborne hyperspectral data is used in combination with linear target simulation procedures to create a representative amount of target-to-background ratios for evaluation of detection limits. Data from two different airborne hyperspectral sensors, AISA Eagle and Hawk, are used to evaluate transferability of band selection when using different sensors. The same target objects were recorded to compare the calculated detection limits. To determine subpixel classification results, pure pixels from the target materials are extracted and used to simulate mixed pixels with selected background materials. Target signatures are linearly combined with different background materials in varying ratios. The commonly used classification algorithms Adaptive Coherence Estimator (ACE) is used to compare the detection limit for the original data with several band selection and data reduction strategies. The evaluation of the classification results is done by assuming a fixed false alarm ratio and calculating the mean target-to-background ratio of correctly detected pixels. The results allow drawing conclusions about specific band combinations for certain target and background combinations. Additionally, generally useful wavelength ranges are determined and the optimal amount of principal components is analyzed.
Hyperspectral remote sensing data can be used for civil and military applications to detect and classify target objects that cannot be reliably separated using broadband sensors. The comparably low spatial resolution is compensated by the fact that small targets, even below image resolution, can still be classified. The goal of this paper is to determine the target size to spatial resolution ratio for successful classification of different target and background materials. Airborne hyperspectral data is used to simulate data with known mixture ratios and to estimate the detection threshold for given false alarm rates. The data was collected in July 2014 over Greding, Germany, using airborne aisaEAGLE and aisaHAWK hyperspectral sensors. On the ground, various target materials were placed on natural background. The targets were four quadratic molton patches with an edge length of 7 meters in the colors black, white, grey and green. Also, two different types of polyethylene (camouflage nets) with an edge length of approximately 5.5 meters were deployed. Synthetic data is generated from the original data using spectral mixtures. Target signatures are linearly combined with different background materials in specific ratios. The simulated mixtures are appended to the original data and the target areas are removed for evaluation. Commonly used classification algorithms, e.g. Matched Filtering, Adaptive Cosine Estimator are used to determine the detection limit. Fixed false alarm rates are employed to find and analyze certain regions where false alarms usually occur first. A combination of 18 targets and 12 backgrounds is analyzed for three VNIR and two SWIR data sets of the same area.
Directional effects in remotely sensed reflectance data can influence the retrieval of plant biophysical and biochemical estimates. Previous studies have demonstrated that directional measurements contain added information that may increase the accuracy of estimated plant structural parameters. Because accurate biochemistry mapping is linked to vegetation structure, also models to estimate canopy nitrogen concentration (CN) may be improved indirectly from using multiangular data. Hyperspectral imagery with five different viewing zenith angles was acquired by the spaceborne CHRIS sensor over a forest study site in Switzerland. Fifteen canopy reflectance spectra corresponding to subplots of field-sampled trees were extracted from the preprocessed CHRIS images and subsequently two-term models were developed by regressing CN on four datasets comprising either original or continuum-removed reflectances. Consideration is given to the directional sensitivity of the CN estimation by generating regression models based on various combinations (n=15) of observation angles. The results of this study show that estimating canopy CN with only nadir data is not optimal irrespective of spectral data processing. Moreover adding multiangular information improves significantly the regression model fits and thus the retrieval of forest canopy biochemistry. These findings support the potential of multiangular Earth observations also for application-oriented ecological monitoring.
An empirical (target-) BRDF normalization method has been implemented for Imaging Spectrometry data processing,
following the approach of Kennedy, published in 1997. It is a simple, empirical method with the purpose of a rapid
technique, based on a least-squares quadratic curve fitting process. The algorithm is calculating correction factors in
either multiplicative or additive manner for each of the identified land cover classes, per spectral band and view angle
unit. Image pre-classification is essential for successful anisotropy normalization. This anisotropy normalization method
is a candidate to be used as baseline correction for future data products of APEX, a new airborne Imaging Spectrometer
suitable for simulation and inter-calibration of data from various other sensors.
A classification algorithm, being able to provide anisotropy class indexing that is optimized for the purpose of BRDF
normalization has to be used. In this study, the performance of the standard Spectral Angle Mapper (SAM) approach
with RSL's spectral database SPECCHIO attached is investigated. Due to its robustness regarding directional effects,
SAM classification is estimated to be the most efficient. Results of both the classification and the normalization process
are validated using two airborne image datasets from the HyMAP sensor, taken in 2004 over the "Vordemwald" test site
in northern Switzerland.
KEYWORDS: Databases, Data storage, Calibration, Remote sensing, Java, Spectral data processing, Human-machine interfaces, Computing systems, Data processing, Imaging systems
The management and storage of spectroradiometer data are important issues, especially in regards of long-term use, data
quality and shareability. The SPECCHIO spectral database system developed at the Remote Sensing Laboratories (RSL)
provides a solution for the organized storage of spectral data and associated metadata and for the spectral processing
based on interactive, customizable and generic processing chains. Optimized data structures and graphical user interfaces
combined with intelligent file parsing routines enable the efficient entry of spectral data and metadata. The system can be
operated in a heterogeneous computing environment, offering multiuser access to a centralized database and enabling
easy data sharing within and even across research groups.
Spectro-directional surface measurements can either be performed in the field or within a laboratory setup. Laboratory measurements have the advantage of constant illumination and neglectable atmospheric disturbances. On the other hand, artificial light sources are usually less parallel and less homogeneous than the clear sky solar illumination. To account for these differences and for determining for which targets a replacement of field by laboratory experiments is indeed feasible, a quantitative comparison is a prerequisite. Currently, there exists no systematic comparison of field and laboratory measurements using the same targets.
In this study we concentrate on the difference in spectro-directional field and laboratory data of the same target due to diffuse illumination. The field data were corrected for diffuse illumination following the proposed procedure by Martonchik . Spectro-directional data were obtained with a GER3700 spectroradiometer. In the field, a MFR sun photometer directly observed the total incoming diffuse irradiance. In the laboratory, a 1000W brightness-stabilized quartz tungsten halogen lamp was used. For the first direct comparison of field and laboratory measurements, we used an artificial and inert target with high angular anisotropy. Analysis shows that the diffuse illumination in the field is leading to a higher total reflectance and less pronounced angular anisotropy.
Since the launch of MERIS on ENVISAT long term activities using vicarious calibration approaches are set in place to monitor potential drifts in calibration in the radiance products of MERIS. We are using a stable, well monitored reference calibration site (Railroad Valley, Nevada, USA) to derive calibration uncertainties of MERIS over time. We are using interpolation of uncertainties to derive a second set of uncertainties for a national data validation in the Netherlands. A satellite image derived land use map of the Netherlands (LGN4) is used to determine the largest homogeneous land use classes using a standard purity index (SPI). Potential adjacency effects are minimized using moving window filters on the pixels of the aggregated map. Multiple error propagation is being used to assess the impact of calibration accuracy on land use classification. A classification in 9 land use classes is finally performed on MERIS FR images of the Netherlands using image based spectral unmixing and matched filtering with endmembers derived from the LGN. We conclude that the classification performance may significantly be increased, when taking into account long-term vicarious calibration results.
The launch of ESA’s ENVISAT in March 2002 was followed by a commissioning phase for all ENVISAT instruments to verify the performance of ENVISAT instruments and recommend possible adjustments of the calibration or the product algorithms before the data was widely distributed. The focus of this paper is on the vicarious calibration of the Medium Resolution Imaging Spectrometer (MERIS) radiance product (Level 1b) over land. From August to October 2002, several vicarious calibration (VC) experiments for MERIS were performed by the Optical Sciences Center, University of Arizona, and the Remote Sensing Laboratories, University of Zurich. The purpose of these activities was the acquisition of in-situ measurements of surface and atmospheric conditions over a bright, uniform land target, preferably during the time of MERIS data acquisition. The experiment was performed on a dedicated desert site (Railroad Valley Playa, Nevada, USA), which has previously been used to calibrate most relevant satellite instruments (e.g., MODIS, ETM+, etc.). In-situ data were then used to compute top-of-atmosphere (TOA) radiances which were compared to the MERIS TOA radiances (Level 1b full resolution product) to determine the in-flight radiometric response of the on-orbit sensor. The absolute uncertainties of the vicarious calibration experiment are found between 3.36-7.15%, depending on the accuracies of the available ground truth data. Based on the uncertainties of the vicarious calibration method and the calibration accuracies of MERIS, no recommendation to update the MERIS calibration is given.
Utilization of sub-pixel targets for radiometric calibration of airborne and space-borne imaging sensors involves the uncertainty of their contribution to the pixel-integrated radiance. This contribution depends not only on the target area but also on an unknown location of the sub-pixel target within a sensor pixel. A technique is proposed to retrieve both the target radiance and its sub-pixel location from the target image, taking into account the effects of the sensor point spread function. The technique was used for in-flight calibration of the thermal channels of the airborne imaging spectrometer DAIS-7915.
A multisensor airborne campaign is carried out in Switzerland in summer 1997. The campaign did not only involve a suite of different sensors but also extensive ground supporting measurements. Amongst the sensor that acquired data over a predefined set of three standard test sites were the hyperspectral imagers DAIS 7915 and CASI, a wide angle airborne camera (WAAC) and a SAR (E-SAR) system as well as an imaging laserscanner. On the ground, geolocation is performed with differential GPS systems and a number of georeferenced ground control points. An active navigation system for the aircraft is used for accurate flight path determination in order to support single- and multi-pass interferometric flights. The thermal ground references consist of a number of targets in the field to verify the thermal performance of the DAIS. Radiometric validation on the ground involves spectroradiometric measurements of a number of selected reference targets, measurements of global flux and radiant temperature, as well as sunphotometer measurements. Conventional field mapping completes the full documentation of the three test sites. The generation of digital surface models using the stereo approach of the WAAC camera and the laserscanner is a goal to support the georeferencing of the different acquired image data. Finally all data are projected onto a common reference system and can be used for further analysis.
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