tailieunhanh - Báo cáo hóa học: " Biologically-inspired data decorrelation for hyperspectral imaging"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Biologically-inspired data decorrelation for hyperspectral imaging | Picon et al. EURASIP Journal on Advances in Signal Processing 2011 2011 66 http content 2011 1 66 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Biologically-inspired data decorrelation for hyper-spectral imaging Artzai Picon1 Ovidiu Ghita2 Sergio Rodriguez-Vaamonde1 Pedro Ma Iriondo3 and Paul F Whelan2 Abstract Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However because of the large dimensionality and complexity of the hyper-spectral data the extraction of robust features image descriptors is not a trivial issue. Thus to facilitate efficient feature extraction decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis PCA linear discriminant analysis LDA wavelet decomposition WD or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical spectral characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification Keywords Hyper-spectral data feature extraction fuzzy sets material classification 1. Introduction Hyper-spectral imaging involves the acquisition see Figure 1 and interpretation of multi-dimensional digital images that are able to sample the spectral .