12 research outputs found
Spectral mixture analysis of EELS spectrum-images
Recent advances in detectors and computer science have enabled the
acquisition and the processing of multidimensional datasets, in particular in
the field of spectral imaging. Benefiting from these new developments, earth
scientists try to recover the reflectance spectra of macroscopic materials
(e.g., water, grass, mineral types...) present in an observed scene and to
estimate their respective proportions in each mixed pixel of the acquired
image. This task is usually referred to as spectral mixture analysis or
spectral unmixing (SU). SU aims at decomposing the measured pixel spectrum into
a collection of constituent spectra, called endmembers, and a set of
corresponding fractions (abundances) that indicate the proportion of each
endmember present in the pixel. Similarly, when processing spectrum-images,
microscopists usually try to map elemental, physical and chemical state
information of a given material. This paper reports how a SU algorithm
dedicated to remote sensing hyperspectral images can be successfully applied to
analyze spectrum-image resulting from electron energy-loss spectroscopy (EELS).
SU generally overcomes standard limitations inherent to other multivariate
statistical analysis methods, such as principal component analysis (PCA) or
independent component analysis (ICA), that have been previously used to analyze
EELS maps. Indeed, ICA and PCA may perform poorly for linear spectral mixture
analysis due to the strong dependence between the abundances of the different
materials. One example is presented here to demonstrate the potential of this
technique for EELS analysis.Comment: Manuscript accepted for publication in Ultramicroscop
A spatial contextual postclassification method for preserving linear objects in multispectral imagery
Classification of remote sensing multispectral data is important for segmenting images and thematic mapping and is generally the first step in feature extraction. Per-pixel classification, based on spectral information alone, generally produces noisy classification results. The introduction of spatial information has been shown to be beneficial in removing most of this noise. Probabilistic label relaxation (PLR) has proved to be advantageous using second-order statistics; here, we present a modified contextual probabilistic relaxation method based on imposing directional information in the joint probability with third-order statistics. The proposed method was tested in synthetic images and real images; the results are compared with a "Majority" algorithm and the classical PLR method. The proposed third-order method gives the best results, both visually and numerically
Development of techniques to classify marine benthic habitats using hyperspectral imagery in oligotrophic, temperate waters
There is an increasing need for more detailed knowledge about the spatial distribution and structure of shallow water benthic habitats for marine conservation and planning. This, linked with improvements in hyperspectral image sensors provides an increased opportunity to develop new techniques to better utilise these data in marine mapping projects. The oligotrophic, optically-shallow waters surrounding Rottnest Island, Western Australia, provide a unique opportunity to develop and apply these new mapping techniques. The three flight lines of HyMap hyperspectral data flown for the Rottnest Island Reserve (RIR) in April 2004 were corrected for atmospheric effects, sunglint and the influence of the water column using the Modular Inversion and Processing System. A digital bathymetry model was created for the RIR using existing soundings data and used to create a range of topographic variables (e.g. slope) and other spatially relevant environmental variables (e.g. exposure to waves) that could be used to improve the ecological description of the benthic habitats identified in the hyperspectral imagery. A hierarchical habitat classification scheme was developed for Rottnest Island based on the dominant habitat components, such as Ecklonia radiata or Posidonia sinuosa. A library of 296 spectral signatures at HyMap spectral resolution (~15 nm) was created from >6000 in situ measurements of the dominant habitat components and subjected to spectral separation analysis at all levels of the habitat classification scheme. A separation analysis technique was developed using a multivariate statistical optimisation approach that utilised a genetic algorithm in concert with a range of spectral metrics to determine the optimum set of image bands to achieve maximum separation at each classification level using the entire spectral library. These results determined that many of the dominant habitat components could be separated spectrally as pure spectra, although there were almost always some overlapping samples from most classes at each split in the scheme. This led to the development of a classification algorithm that accounted for these overlaps. This algorithm was tested using mixture analysis, which attempted to identify 10 000 synthetically mixed signatures, with a known dominant component, on each run. The algorithm was applied directly to the water-corrected bottom reflectance data to classify the benthic habitats. At the broadest scale, bio-substrate regions were separated from bare substrates in the image with an overall accuracy of 95% and, at the finest scale, bare substrates, Posidonia, Amphibolis, Ecklonia radiata, Sargassum species, algal turf and coral were separated with an accuracy of 70%. The application of these habitat maps to a number of marine planning and management scenarios, such as marine conservation and the placement of boat moorings at dive sites was demonstrated.
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