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Successful "cracking" of bit-level security compromises network integrity and physical layer augmentation is being investigated to improve overall security. Intra-cellular security is addressed here using device-specific RF "Distinct Native Attribute" (RF-DNA) fingerprints in a localized regional air monitor, with targeted applications including cellular networks such as the Global System for Mobile (GSM) Communications and last mile Worldwide Interoperability for Microwave Access (WiMAX) systems. Previous work demonstrated GSM intermanufacturer classification (manufacturer discrimination) using RF-DNA fingerprinting and achieved accuracies of 92% at SN R = 6 dB. These results are extended here for intramanufacturer classification (serial number discrimination). Historically, intra-manufacturer discrimination has posed the greatest challenge and RF-DNA fingerprinting has been effective with both Orthogonal Frequency Division Multiplexed (OFDM) and Direct Sequence Spread Spectrum (DSSS) network signals. Intra-manufacturer GSM results are provided here based on identical signal collection, fingerprint generation, and MDA/ML classification processes used for previous inter-manufacturer assessment. When comparing performance, the trend for GSM intra-manufacturer classification is consistent with previous work for other network-based signals and device classification is much more challenging. For classification accuracies of 80% or better, intra-manufacturer fingerprinting requires an increase of 20 − 25 dB in SN R to achieve inter-manufacturer performance.

Reference data ("ground truth") maps have traditionally been used to assess the accuracy of classification algorithms. These maps typically classify pixels or areas of imagery as belonging to a finite number of ground cover classes, but do not include sub-pixel abundance estimates; therefore, they are not sufficiently detailed to directly assess the performance of spectral unmixing algorithms. Our research aims to efficiently generate, validate, and apply abundance map reference data (AMRD) to airborne remote sensing scenes. Scene-wide AMRD for this study were generated using the remotely sensed reference data (RSRD) technique, which spatially aggregates classification or unmixing results from fine scale imagery (e.g., 1-m GSD) to co-located coarse scale imagery (e.g., 10-m GSD or larger). Validation of the accuracy of these methods was previously performed for generic 10 m × 10 m coarse scale imagery, resulting in AMRD with known accuracy. The purpose of this paper was to apply this previously validated AMRD to specific examples of airborne coarse scale imagery. Application of AMRD involved three main parts: (1) spatial alignment of coarse and fine scale imagery; (2) aggregation of fine scale abundances to produce coarse scale imagery specific AMRD; and (3) demonstration of comparisons between coarse scale unmixing abundances and AMRD. Spatial alignment was performed using our new scene-wide spectral comparison (SWSC) algorithm, which aligned imagery with accuracy approaching the distance of a single fine scale pixel. We compared simple rectangular aggregation to coarse sensor point-spread function (PSF) aggregation, and found that PSF returned lower error, but that rectangular aggregation more accurately estimated true AMRD at ground level. We demonstrated various metrics for comparing unmixing results to AMRD, including several new techniques which adjust for known error in the reference data itself. These metrics indicated that fully constrained linear unmixing of AVIRIS imagery across all three scenes returned an average error of 10.83% per class and pixel. Our reference data research has demonstrated a viable methodology to efficiently generate, validate, and apply AMRD to specific examples of airborne remote sensing imagery, thereby enabling direct quantitative assessment of spectral unmixing performance.

Abstract:The purpose of this study is to validate the accuracy of abundance map reference data (AMRD) for three airborne imaging spectrometer (IS) scenes. AMRD refers to reference data maps ("ground truth") that are specifically designed to quantitatively assess the performance of spectral unmixing algorithms. While classification algorithms typically label whole pixels as belonging to certain ground cover classes, spectral unmixing allows pixels to be composed of fractions or abundances of each class. The AMRD validated in this paper were generated using our previously-proposed remotely-sensed reference data (RSRD) technique, which spatially aggregates the results of standard classification or unmixing algorithms from fine spatial-scale IS data to produce AMRD for co-located coarse-scale IS data. Validation of the three scenes was accomplished by estimating AMRD in 51 randomly-selected 10 m×10 m plots, using seven independent methods and observers. These independent estimates included field surveys by two observers, imagery analysis by two observers and RSRD by three algorithms. Results indicated statistically-significant differences between all versions of AMRD. Even AMRD from our two field surveys were significantly different for two of the four ground cover classes. These results suggest that all forms of reference data require validation prior to use in assessing the performance of classification and/or unmixing algorithms. Given the significant differences between the independent versions of AMRD, we propose that the mean of all (MOA) versions of reference data for each plot and class is most likely to represent true abundances. Our independent versions of AMRD were compared to MOA to characterize error and uncertainty. Best case results were achieved by a version of imagery analysis, which had mean coverage area differences of 2.0%, with a standard deviation of 5.6%. One of the RSRD algorithms was nearly as accurate, achieving mean differences of 3.0%, with a standard deviation of 6.3%. Further analysis of statistical equivalence yielded an overall zone of equivalence between [−7.0%, 7.2%] for this version of RSRD. The relative accuracy of RSRD methods is promising, given their potential to efficiently generate scene-wide AMRD. These results provide the first known validated abundance level reference data for airborne IS data.

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