Detection and Identification Accuracy of PCA-Accelerated Real-Time Processing of Hyperspectral Imagery
This work addresses the need for faster processing in hyperspectral imagery applications, but it is incremental as it builds on existing PCA methods.
The paper tackles the challenge of speeding up real-time hyperspectral image processing by determining how many principal components can be omitted in PCA-based dimension reduction without significantly affecting detection and identification accuracy. They found that the number of principal components can be reduced substantially before detection rates noticeably change.
Real-time or near real-time hyperspectral detection and identification are extremely useful and needed in many fields. These data sets can be quite large, and the algorithms can require numerous computations that slow the process down. A common way of speeding up the process is to use principal component analysis (PCA) for dimension reduction. In the reduced dimensional space, provided by a subset of the principal components, fewer computations are needed to process the data resulting in a faster run time. In this paper, we propose a way to further decrease the time required to use PCA by investigating how many principal components may be omitted with minimal impact on the detection rate. Using ACE to perform the detection, and then probability, and spectral fit for identification, we find that the number of principal components can be reduced by a substantial amount before seeing a noticeable change in detection rates.