This paper presents a simulation study and an experimental evaluation of a novel imaging spectroscopy technique, where multi-channel image data are acquired instantaneously and transformed into spectra. A digital colour camera equipped with an additional colour filter array was used to acquire an instantaneous single image that was demosaicked to generate a multi-channel image. A statistical tr...

Locally preserving projection (LPP) does not take advantage of the spatial correlation of pixels in the image, and the pixels are considered as independent pieces of information. In this paper, a kernel based manifold learning feature extraction method which considers spatial relationship of neighboring pixels, called supervised composite kernel locality preserving projection (SCKLPP), is propo...

This paper proposes a method of reducing dimensions based on genetic algorithm and object-oriented classification based on support vector machine (SVM). The basic idea is subspace decomposition of hyperspectral images at first, then selecting suitable bands in each subspace by using genetic algorithm and putting all selected bands of each subspace together. Furthermore, the hyperspectral image ...

Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called...

Hyperspectral band image selection is a fundamental problem for hyperspectral remote sensing data processing. Accepting its importance, several information-based band selection methods have been proposed, which apply Shannon entropy to measure image information. However, the Shannon entropy is not accurate in measuring image information since it neglects the spatial distribution of pixels and i...

Image processing of hyperspectral image sector shows a thriving upbeat in innovation of new and novel techniques. For obvious reasons, most of these apply to the process of image segmentation and classification, in which is the heart of image processing. Augmented use of hyperspectral images puts forth a hectic workload that needs to deal with spatial data imposing large memory and computing re...

The need for fast hyperspectral data processing methods is discussed. Discussion includes the necessity of faster processing techniques in order to realize emerging markets for hyperspectral data. Several standard hyperspectral image processing methods are presented, including maximum likelihood classification, principal components analysis, and canonical analysis. Modifications of those method...

Hyperspectral image is a sequence of images generated by hundreds of detectors. Each detector is sensitive only to a narrow range of wavelengths. One can view such an image sequence as a three-dimensional array of intensity values (pixels) within a rectangular prism. This image prism reveals contiguous spectrum information about the composition of the area being viewed by the instrument. The pr...

Hyperspectral imaging is an important visual modality with growing interest and range of applications. The latter, however, is hindered by the fact that existing devices are limited in either spatial, spectral, and/or temporal resolution, while yet being both complicated and expensive. We present a low cost and fast method to recover high quality hyperspectral images directly from RGB. Our appr...

Background: Hyperspectral image analysis is a fast and non-destructive technique that is being used to measure quality attributes of food products. This research investigated the feasibility of predicting internal quality attributes, such as Total Soluble Solids (TSS), pH, Titratable Acidity (TA), and maturity index (TSS/TA); and external quality attributes such as color components (L*, a*, b*)...