This paper reports the development of a spectral reconstruction technique for predicting hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for ...

Hyperspectral imaging and analysis refers to the capture and understanding of image content in multiple spectral channels. Satellite and airborne hyperspectral imaging has been the focus of research in remote sensing applications since nearly the past three decades. Recent use of ground-based hyperspectral imaging has found immense interest in areas such as medical imaging, art and archaeology,...

Hyperspectral imagery is a new type of high-dimensional image data which is now used in many Earth-based and planetary exploration applications. Many efforts have been devoted to designing and developing compression algorithms for hyperspectral imagery. Unfortunately, most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately model...

Hyperspectral images can be represented either as a set of images or as a set of spectra. Spectral classification and segmentation and data reduction are the main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation approach with an appropriate hiearchical model with hidden markovian variables which gives the possibility to jointly do data reduction, spectral...

Hyperspectral imaging comprises the technologies that incorporates remote sensing and analysis of an object or specific area of the earth at different distances with very large number of bands. Currently, a wide range of hyperspectral data sets are obtained continuously, in addition to conventional multispectral remote sensing images, and presented to users by institutions for both commercial a...

Imaging devices are of increasing use in environmental research requiring an urgent need to deal with such issues as image data, feature matching over different dimensions. Among them, matching hyperspectral image with other types of images is challenging due to the high dimensional nature of hyperspectral data. This chapter addresses this problem by investigating structured support vector mach...

In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work...

Desertification is a worldwide concern and the assessment of aeolian desertification has become one hotspot in global ecosystem research. In this paper, hyperspectral data acquired from modular OMIS-I imaging spectrometer, combined with ETM data and field survey data, was used to assess the aeolian desertification in Korqin Sand, Inner Mongolia, China by pixel-level. The results indicated that ...

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should gre...

We propose resolution progressive Three-Dimensional Set Partitioned Embedded bloCK (3D-SPECK), an embedded wavelet based algorithm for hyperspectral image compression. The proposed algorithm also supports random Region-Of-Interest (ROI) access. For a hyperspectral image sequence, integer wavelet transform is applied on all three dimensions. The transformed image sequence exhibits a hierarchical...