Noise-tolerant Hyperspectral Signature Classification in Unresolved Object Detection with Adaptive Tabular Nearest Neighbor Encoding

Accurate spectral signature classification is a crucial step in the nonimaging detection and recognition of spaceborne objects. In classical hyperspectral recognition applications, especially where linear mixing models are employed, signature classification accuracy depends on accurate spectral endmember discrimination. In selected target recognition (ATR) applications, previous non-adaptive techniques for signature classification have yielded class separation and classifier refinement results that tend to be suboptimal. In practice, the number of signatures accurately classified often depends linearly on the number of inputs. This can lead to potentially severe classification errors in the presence of noise or densely interleaved signatures. In this paper, we present an enhancement of an emerging technology for nonimaging spectral signature classification based on a highly accurate, efficient search engine called Tabular Nearest Neighbor Encoding (TNE). Adaptive TNE can optimize its classifier performance to track input nonergodicities and yield measures of confidence or caution for evaluation of classification results. Unlike neural networks, TNE does not have a hidden intermediate data structure (e.g., a neural net weight matrix). Instead, TNE generates and exploits a user-accessible data structure called the agreement map (AM), which can be manipulated by Boolean logic operations to effect accurate classifier refinement through programmable algorithms. The open architecture and programmability of TNE’s pattern-space (AM) processing allows a TNE developer to determine the qualitative and quantitative reasons for classification accuracy, as well as characterize in detail the signatures for which TNE does not obtain classification matches, and why such mis-matches occur. In this study AM-based classification has been modified to partially compensate for input statistical changes, in response to performance metrics such as probability of correct classification (Pd) and rate of false detections (Rfa). Adaptive TNE can thus achieve accurate signature classification in the presence of time-varying noise, closely spaced or interleaved signatures, and imaging system optical distortions. We analyze classification accuracy of closely spaced spectral signatures adapted from a NASA database of space material signatures. Additional analysis pertains to computational complexity and noise sensitivity, which are superior to non-adaptive TNE or Bayesian techniques based on classical neural networks.