Biologically-inspired data decorrelation for hyper-spectral imaging
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RESEARCH
Open Access
Biologically-inspired data decorrelation for hyperspectral 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 properties of the materials associated with the visualized objects [1]. Nowadays, the current range of spectral imaging systems is able to capture multiple bands from ultraviolet to far infrared with good bandwidth resolution. This increased flexibility in the image acquisition process prompted the inclusion of the hyper-spectral imaging systems in the development of a wide variety of computer vision systems, such as video surveillance, food inspection, medical imaging, remote sensing, and material classification [2-4]. The main characteristic of the hyper-spectral images is that each pixel is defined by a multi-dimensional vector whose elements are the spectral (electromagnetic or wavelengths) components that are captured from the light arriving at the spectral sensor. In this regard, the * Correspondence: [email protected] 1 Information and Interaction Systems Unit, Tecnalia, Zamudio, Bizkaia, Spain Full list of author information is available at the end of the article
hyper-spectral imaging sensors (or spectrographs) allow the extraction of a richer source of information beyond the visible spectral domain (that is usually captured by a standard color camera), a fact that opens the possibility to analyze not
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