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.
The work is concerned with assessing the health status of trees of the Norway spruce species using airborne hyperspectral (HS) data (HyMap). The study was conducted in the Sokolov basin in the western part of the Czech Republic. First, statistics were employed to assess and validate diverse empirical models based on spectral information using the ground truth data (biochemically determined chlorophyll content). The model attaining the greatest accuracy (D718/D704∶RMSE = 0.2055 mg/g, R2 = 0.9370) was selected to produce a map of foliar chlorophyll concentrations (Cab). The Cab values retrieved from the HS data were tested together with other nonquantitative vegetation indicators derived from the HyMap image reflectance to create a statistical method allowing assessment of the condition of Norway spruce. As a result, we integrated the following HyMap derived parameters (Cab, REP, and SIPI) to assess the subtle changes in physiological status of the macroscopically undamaged foliage of Norway spruce within the four studied test sites. Our classification results and the previously published studies dealing with assessing the condition of Norway spruce using chlorophyll contents are in a good agreement and indicate that this method is potentially useful for general applicability after further testing and validation.
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.