Spectral matching approaches in hyperspectral image processing

Many spectral matching algorithms, ranging from the traditional clustering techniques to the recent automated matching models, have evolved. This paper provides a review and up-to-date information on the past and current role of the spectral matching approaches adopted in hyperspectral satellite image processing. The need for spectral matching has been deliberated and a list of spectral matching algorithms has been compared and described. A review of the conventional spectral angle measures and the advanced automated spectral matching tools indicates that, for better performance of target detection, there is a need for combining two or more spectral matching techniques. From the studies of several authors, it is inferred that continuous improvement in the matching techniques over the past few years is due to the need to handle and analyse hyperspectral image data for various applications. The need to develop a well-built and specialized spectral library to accommodate the resources from enormous spectral data is suggested. This may improve accuracy in mineral and soil mapping, vegetation species identification and health monitoring, and target detection. The future role of cloud computing in accessing globally distributed spectral libraries and performing spectral matching is highlighted. Rather than inferring that a particular matching algorithm is the best, this paper points out the requirements of an ideal algorithm. With increasing usage of hyperspectral data for resources mapping, the review presented in this paper will certainly benefit the large and emerging community of hyperspectral image users.