Results Of Spectral Classification Research Articles

A Spectral–Spatial Feedback Close Network System for Hyperspectral Image Classification

This paper presents a new spectral–spatial (SS) approach to hyperspectral image classification (HSIC), called SS feedback close network system (SSFCNS), which has not been explored in the past. Unlike commonly used SS-based methods SSFCNS includes a feedback close network system (FCNS) to obtain spatial information via a selective spatial filter in an iterative manner. More specifically, SSFCNS takes advantage of FCNS which utilizes a particularly selected spatial filter to capture a posteriori spatial information directly from spectral-classified data samples and then feeds back such obtained spatial-filtered image to be combined with the current image cube to create a new image cube that can be used as a new input to re-implement SSFCNS. The process is carried out in such a way that the spatial information obtained from spectral classification results is updated by FCNS iteratively and terminated by a Tanimoto index (TI)-derived automatic stopping rule. To evaluate the performance of SSFCNS several spatial filters (i.e., Gaussian, bilateral, guided, and Gabor filters) are explored for real image experiments. The experimental results demonstrate that SSFCNS performs significantly better in classification accuracy compared to SS-based methods which do not use FCNS.

Hyperspectral classification via deep networks and superpixel segmentation

This article presents a new hyperspectral image classification method, which is capable of automatic feature learning while achieving high classification accuracy. The method contains the following two major modules: the spectral classification module and the spatial constraints module. Spectral classification module uses a deep network, called ‘Stacked Denoising Autoencoders’ (SdA), to learn feature representation of the data. Through SdA, the data are projected non-linearly from its original hyperspectral space to some higher-dimensional space, where more compact distribution is obtained. An interesting aspect of this method is that it does not need any prior feature design/extraction process guided by human. The suitable feature for the classification is learnt by the deep network itself. Superpixel is utilized to generate the spatial constraints for the refinement of the spectral classification results. By exploiting the spatial consistency of neighbourhood pixels, the accuracy of classification is further improved by a big margin. Experiments on the public data sets have revealed the superior performance of the proposed method.

Information fusion of aerial images and LIDAR data in urban areas: vector-stacking, re-classification and post-processing approaches

This research investigates information fusion approaches of high-resolution aerial images and elevation data from Light Detection and Ranging (LIDAR) for urban-environment mapping. Three feature fusion methods are proposed and compared: (1) the vector-stacking approach that combines spectral and LIDAR features in one classifier; (2) the re-classification approach that firstly processes spectral signals in a classifier and then integrates its output with LIDAR features to obtain the final result and (3) the post-processing approach that uses the LIDAR data to refine the results of spectral classification. The height features used in the above three algorithms are extracted from the LIDAR digital surface model (DSM) image; these include elevation difference, maximum and minimum values, variance and the grey-level co-occurrence matrix (GLCM) textures. In addition, the average height from object-based segmentation is also computed. In the experiments, support vector machines (SVMs) are used as classifiers for all fusion schemes due to their capability and robustness for many classification problems. The three algorithms are evaluated using a 40-cm spatial resolution digital orthophoto and the corresponding LIDAR data of Odense, Denmark. In the experiments, the vector-stacking method with the Maximum–Minimum (Max–Min) feature, the re-classification method with the Max–Min feature and the post-processing approach obtain promising results (94.7%, 95.0% and 94.6%, respectively), which are significantly higher than the spectral-only classification (82.5%).

Infrared Spectra of Brown Dwarf Candidates in Taurus

We have obtained low resolution infrared spectra of 21 brown dwarf candidates, selected from a survey performed at the Canada-France-Hawaii Telescope. The spectra were obtained at the Telescopio Nazionale Galileo (La Palma), using the NICS spectrometer in the Amici configuration, which allows to observe the range 0.85–2.45 μm in a single shot. The bands shown by most of the spectra confirm that we are dealing with low effective temperature objects. Preliminary results of spectral classification are presented.

SPECTRAL CLASSIFICATION CRITERIA FOR SOME EARLY TYPE STARS IN THE UV REGION

The aim of this paper is to get new critertia for spectral classification of some early type stars which depend on the flux in the UV region λ λ 1500–2500 by carrying out spectrophotometric analysis of observational ultraviolet data of stars obtained by the S2/68 Ultraviolet Sky Survey Telescope (UVSST) aboard the European Space Research Organization (ESRO) Satellite TD1. We have developed these new criteria based on the Intrinsic Ultraviolet Colour Index (IUI), and the Intrinsic Flux Ratio (IFR). Using these quantities we are going to represent the results of spectral classification of 323 early type stars mainly from spectral type B and A. The results of calculations of the Intrinsic Flux Ratios for the stars under investigation together with their Colour Temperatures (Tc) are given. Comparison between our suggested two new criteria with the MK classification system and Cucchairo (1980) classification system was carried out.

Infrared spectroscopy of dystrophic mdx mouse muscle tissue distinguishes among treatment groups.

Four groups of mdx mice (deflazacort, high dose of 1.5 mg/kg and low dose of 0.75 mg/kg; prednisone, 1.0 mg/kg; and a placebo) were examined in a double-blind protocol. The experiments tested the hypothesis that infrared spectroscopy can distinguish among gastrocnemius muscle tissues derived from dystrophic animals (n = 22) from different treatment groups and from control muscle tissue (n = 23). Results showed that muscle, inflamed muscle, and tendon can be distinguished on the basis of their infrared absorption patterns. Distinctions among the spectra of the four treatment groups were sought with automated pattern-recognition methods. These classification methods, based either on spectral regions (900-1,500 cm-1) or on principal-component analysis, were in close agreement, assigning 15 or 16, respectively, of 22 mdx spectra to the correct treatment group. Both trials cleanly separated the high-dose deflazacort from the placebo group of muscles, whereas the prednisone and low-dose deflazacort groups were persistently confused in these classifications. Changes in the histology of muscle inflammation paralleled the spectral-classification results. Thus the proposed method, combining infrared spectroscopy with pattern-recognition algorithms, can distinguish treatment effects on muscle tissue. Specific spectral features characteristic of tissue type, disease progression, and treatment effects are not yet elucidated.