Hyperspectral Analysis Research Articles

Rock-type identification in a simulated underground environment using second-derivative thermal infrared reflectance spectra

This study investigates the usefulness of thermal infrared reflectance spectroscopy (TIR) to map rock faces in an underground environment. A laboratory Fourier transform infrared (FTIR) spectrometer was used to measure reflectance spectra (500–5000 cm−1) of freshly broken rock surfaces. A total of 37 samples covering 10 different rock types, including barren silicate rocks and rocks with disseminated sulfides, was collected from eight mines around the Sudbury Basin in Ontario. Freshly broken rock surfaces were measured wet and dry to address environmentalconditionsencounteredunder-ground. Spectral data were processed by the second-derivative approach and the spectral angle mapper algorithm (SAM) for rock-type identification. An average of 77% accuracy was achieved for all rock types. The success of this effort implies that hyperspectral analysis using second-derivative spectra is effective to remove the effect of liquid water, local geometry, and disseminated sulfides while preserving diagnostic rock spectral signatures between 700 and 1300 cm−1.

Mixture density separation as a tool for high-quality interpretation of multi-source remote sensing data and related issues

The end user often needs to define extremely complex interpretation tasks and to require the analysis results to be quantitatively proven for each pixel without the test data. To this end, this paper extends the ideas underlying the model-based unsupervised classification method previously proposed by us (Koltunov and Ben-Dor 2001). Consistently with that method, the quality of assigning a pixel to a cluster is defined as the lower confidence bound (l.c.b.) of the corresponding posterior probability estimate. We propose to compute the l.c.b.s in an approximate way using the Fisher information matrix instead of the bootstrap scheme suggested previously, leading to an l.c.b.-estimation procedure that is faster by a factor of hundreds to thousands, while being reasonably accurate. The issue of selecting the number of clusters is considered in accordance with the quantitative requirements for the level of detail and the reliability of the thematic interpretation. Specifically, the l.c.b.s form a novel selection criterion that allows the most detailed landscape descriptions to be provided with at least a pre-specified value of confidence. This implies detection of highly overlapping clusters, leading to very detailed segmentations. Consequently, numerous thematic classification and object detection problems can be solved, based on single clustering and assuming that thematic classes are unions of components. Then the thematic classification accuracy can be computed in a well-founded manner for each separate pixel, using the obtained covariance matrices of the posterior probability estimators of component membership. The procedures for thematic mapping and object detection are described. The accuracy of the l.c.b. estimation and stability of the new criterion in choosing the number of clusters are illustrated on simulated datasets. The hyperspectral data analysis experiment performed demonstrates part of the developments described in this paper. Several issues that are relevant for remote sensing data interpretation are addressed constructively. In particular, we draft the novel algorithms that use model-based cluster analysis for detection and recognition of remotely sensed objects based on prior information on their size and shape. In addition, we introduce a generalized approach to unsupervised feature extraction from data acquired by a plurality of sensors of different physical nature. A new data model generalizing the traditional Gaussian mixture model is also presented.

Hyperspectral analysis of the ultramafic complex and adjacent lithologies at Mordor, NT, Australia

The Mordor Complex consists of a series of potassic ultramafic rocks which were intruded into Proterozoic felsic gneisses and amphibolite and are overlain by quartzite and unconsolidated deposits. In situ and laboratory 0.4 to 2.5 μm reflectance spectra show Al–OH absorption features caused by absorption in muscovite, kaolinite, and illite/smectite in syenite, granitic gneiss, quartzite and unconsolidated sedimentary deposits, and Fe,Mg–OH features due to phlogopite, biotite, epidote, and hornblende in the mafic and ultramafic rocks. Ferrous-iron absorption positioned near 1.05 μm is most intense in peridotite reflectance spectra. Ferric-iron absorption is intense in most of the felsic lithologies. HyMap data were recorded in 126 narrow bands from 0.43 to 2.5 μm along a 7-km-wide swath with approximately 6-m spatial resolution. Correction of the data to spectral reflectance was accomplished by reference to in situ measurements of an extensive, alluvial plain. Spectral classes for matched filter processing were selected by using the pixel purity index procedure and analysis of in situ and laboratory spectra. Considering the spatial distribution of the resulting 14 classes, some classes were combined, which produced eight classes characterized by Al–OH absorption features, and three Fe,Mg–OH absorption-feature classes. Comparison of the distribution of these 11 spectral classes to a generalized lithologic map of the study area shows that the spectral distinction among the eight Al–OH classes is related to variations in primary lithology, weathering products, and vegetation density. Quartzite is represented in three classes, syenite corresponds to a single scattered class, quartz–muscovite–biotite schist defines a single very coherent class, and unconsolidated sediments are portrayed in four classes. The three mafic-ultramafic classes are distinguished on the basis of generally intense Fe,Mg–OH and ferrous-iron absorption features. A single class represents the main Mordor ultramafic mass. Epidote-bearing rocks define another class, which corresponds to biotite gneiss and, in the southern part of the area, to fracture zones. The third class, which exhibits Al–OH, as well as Fe,Mg–OH features, represents hornblende gneiss and other mafic gneisses. These results indicate the importance of analyzing the VNIR and SWIR spectral shape and albedo, as well as analyzing specific spectral features, for mapping lithologic units in this weathered terrain.

P‐58: Imaging Polarization Interferometer for Flat Panel Display Characterization

AbstractThe next generation of MURATest will be able to make a hyper spectral analysis to get a high definition image with adjustable wavelength resolution. This paper introduces the principle of the new system, based on an imaging polarizing interferometer. The phase shift is realized by two symmetrical Wollaston prisms. The system produces an interference pattern directly on the CCD camera. The phase shift is linearly changed for all pixels using a mechanical translation.

A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles

This paper presents a new approach to the analysis of hyperspectral images, a new class of image data that is mainly used in remote sensing applications. The method is based on the generalization of concepts from mathematical morphology to multi-channel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Theoretical definitions of extended morphological operations are used in the formal definition of the concept of extended morphological profile, which is used for multi-scale analysis of hyperspectral data. This approach is particularly well suited for the analysis of image scenes where most of the pixels collected by the sensor are characterized by their mixed nature, i.e. they are formed by a combination of multiple underlying responses produced by spectrally distinct materials. Experimental results demonstrate the applicability of the proposed technique in mixed pixel analysis of simulated and real hyperspectral data collected by the NASA/Jet Propulsion Laboratory Airborne Visible/Infrared Imaging Spectrometer and the DLR Digital Airborne (DAIS 7915) and Reflective Optics System Imaging Spectrometers. The proposed method works effectively in the presence of noise and low spatial resolution. A quantitative and comparative performance study with regards to other standard hyperspectral analysis methodologies reveals that the combined utilization of spatial and spectral information in the proposed technique produces classification results which are superior to those found by using the spectral information alone.

Hyperspectral analysis of chlorophyll content and photosynthetic capacity of coral reef substrates

Few studies have assessed the biophysical properties controlling reflection and absorption of light in coral reef environments and their relationships with quantitative measures of reef health and productivity. The present article examines the relationship between spectral reflectance, photosynthetic capacity, and chlorophyll a from common coral reef substrates. Reflectance readings of several targets (massive corals Montipora sp., n = 49, and Porites sp., n = 80; macroalgae Chlorodesmis sp., n = 24; and sediment interspersed with benthic microalgae, n = 35) were obtained in situ on Heron Reef, southern Great Barrier Reef (23°27'S, 151°55'E). Measurements of photosynthetic capacity and chlorophyll content were acquired simultaneously. Linear correlations were examined between spectral reflectance at all wavelengths and both photosynthetic capacity and pigment content (Chl a). Reflectance plots for all targets exhibited an absorption feature centered at 675 nm, and spectral reflectance at this wavelength decreased with increasing Chl a levels. The strength of this correlation varied between features, being highest for Porites sp. and lowest for sediment, highlighting the complexities of coral reef environments and the difficulties associated with relating spectral reflectance to biophysical properties. Photosynthetic capacity did not exhibit statistically significant correlations to spectral reflectance or absorption at any wavelength. Our results demonstrate the capabilities and difficulties associated with field scale hyperspectral data for measuring select biophysical properties of coral reefs and the need for assessment of the capabilities of airborne and satellite imaging sensors for similar purposes.

Hyperspectral Mapping of Mineral Assemblages Associated with Gold Mineralization in the Central Pilbara, Western Australia

The Indee district, located in the Central Pilbara tectonic zone of the North Pilbara terrain in Western Australia, contains Archean lode gold deposits and epithermal gold systems. Hydrothermal alteration assemblages associated with these deposits contain the minerals pyrophyllite, white mica, chlorite, and tourmaline. Known prospects are surrounded by flat areas of poorly exposed calcretized bedrock, calcretes, rock float, and sandy soils. Two adjacent lines of airborne hyperspectral data (HyMap®) were acquired. To produce mineral abundance maps, these data were processed to correct for atmospheric and illumination conditions and then calibrated to reflectance data using field spectra. This analysis successfully mapped the distribution of pyrophyllite, white mica, Mg chlorite, Fe chlorite, calcite, dolomite, kaolinite, tourmaline, hematite, and goethite. Alteration assemblages associated with the Camel 2 deposit, which is located along the Mallina shear zone, were found to be characterized by pyrophyllite and Al-rich white mica, the latter having an AlOH absorption feature at 2.194 μ m. In contrast, alteration assemblages associated with epithermal deposits south of the Mallina shear zone are characterized by Al-poor white mica with a longer wavelength absorption feature at 2.210 μ m, the shift reflecting the change in Al chemistry (Tschermak substitution). Tourmaline is also present near the epithermal-like deposits, suggesting that it may be an integral part of these systems. Fe chlorite is associated with both the mesothermal and epithermal systems but is also pervasive as a regional metamorphic mineral. Regolith-related carbonates are common throughout the area and their compositions are determined by the hyperspectral processing. Mineral abundance images were verified by PIMA® (portable infrared mineral analyser) analysis of ground samples, and assays of rocks in previously unknown areas of pyrophyllite and white mica confirmed the presence of anomalous gold. These results show that mineral maps and compositional information derived from hyperspectral analysis are extremely valuable for exploration, even in poorly exposed, regolith-dominated districts such as Indee.

An adaptive method for combined covariance estimation and classification

In this paper, a family of adaptive covariance estimators is proposed to mitigate the problem of limited training samples for application to hyperspectral data analysis in quadratic maximum likelihood classification. These estimators are the combination of adaptive classification procedures and regularized covariance estimators. In these proposed estimators, the semi-labeled samples (whose labels are determined by a decision rule) are incorporated in the process of determining the optimal regularized parameters and estimating those supportive covariance matrices that formulate final regularized covariance estimators. In all experiments with simulated and real remote sensing data, these proposed combined covariance estimators achieved significant improvement on statistics estimation and classification accuracy over conventional regularized covariance estimators and an adaptive maximum likelihood classifier (MLC). The degree of improvement increases with dimensions, especially for ill-posed or very ill-posed problems where the total number of training samples is smaller than the number of dimensions.

DETECTION OF DEFECTS ON SELECTED APPLE CULTIVARS USING HYPERSPECTRAL AND MULTISPECTRAL IMAGE ANALYSIS

Apple defects cause food safety concerns touching the general public and strongly affect the commodity market.Because accumulations of human pathogens are usually observed on surface lesions, detection of lesions is essential forassuring quality and safety. This article presents the application of hyperspectral image analysis to the development ofmultispectral techniques for the detection of defects on three apple cultivars: Golden Delicious, Red Delicious, and Gala.Two steps were performed: (1) hyperspectral image analysis to characterize spectral features of apples for the specificselection of filters to design the multispectral imaging system and (2) multispectral imaging for rapid detection of applecontaminations. Good isolation of scabs, fungal, soil contaminations, and bruises was observed with hyperspectral imagingusing either principal component analysis or the chlorophyll absorption peak. This hyperspectral analysis allowed thedetermination of three spectral bands capable of separating normal from contaminated apples. These spectral bands wereimplemented in a multispectral imaging system with specific band pass filters to detect apple contaminations. In thispreliminary work with 153 samples, good separation between normal and contaminated apples was obtained for Gala (95%)and Golden Delicious (85%). However, separations were limited for Red Delicious (76%).

Subtropical Tree Recognition with Hyperspectral Data Analysis in Hong Kong

Subtropical environment is characterized by a great diversity of flora that is increasingly vulnerable to human impact. The availability of hyperspectral data is going to insert new insight for environmental studies in this environment. In this study, subtropical tree species were studied based on a controlled experiment Spectra of 25 tree species were collected from which the original spectra together with their first and second derivatives were used for tree species recognition. Spectra were collected in four seasons using a high resolution spectrometer taking data in 689 bands from 400-900 nm. The data were filtered to 138 bands for tree recognition using both linear discriminant analysis and a feed forward neural network with back propagation training. Neural network performed better but was relatively inefficient due to long computation time. Original spectra produced high overall accuracy than the derivative data. Results from stepwise discriminant analysis showed that hyperspectral data collected in winter and autumn were able to attain an overall accuracy of over 85% and were significantly better than those taken in spring or summer.

Photo Ecometrics for Forest Inventory

In this paper, we report the results obtained from the application of digital photogrammetry and hyperspectral data analysis for forest inventory purposes. Our long term goal is to provide low-cost yet accurate estimates of as many important forest biophysical parameters as can be measured and inferred with airborne digital cameras. Accuracies of traditional multispectral image analysis algorithms of remotely sensed data are low. Traditional photo interpretation is error prone and expensive. We propose new image analysis strategies that make use of the 3D spatial morphological information from stereo images and the multispectral, texture and contextual information inherent in the imagery. Research on the use of 3D crown shape information in automated tree species recognition has not been reported before. The minimum requirements of image spatial resolution for deriving estimates of tree heights and crown size with high accuracies are not known. With digital photogrammetry, it has been proven that digital camera images can be georeferenced and orthorectified to an accuracy of one to several meters allowing for selecting ground control points directly from digital camera images for georectification of other images including high resolution satellite images. With the georeferenced and orthorectified digital camera images and the above parameters accurately determined, we can detect changes of species composition, height, crown closure, and diameter. These same techniques will significantly improve our ability to economically assess the accuracy of thematic vegetation maps.

Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra

Abstract A Portable Infrared Mineral Analyzer II (PIMA II) field spectrometer was used to measure infrared reflectance spectra (1·3-2·5 μm) of split drill core at 1 cm intervals in both the along-core and cross-core directions. These data were formatted into an image cube similar to that acquired by an imaging spectrometer with 600 spectral channels, and multi-spectral and hyperspectral analysis techniques were used for analysis. Colour images and enhancements provided visual displays of the spectral information, while real-time digital extraction of individual spectra allowed identification of minerals. Absorption band-depth mapping and spectral classification were used to map the spatial distribution of specific minerals in the core. Linear spectral unmixing provided estimated mineral abundances. Analysis results demonstrate that multi-spectral and hyperspectral image analysis methods can be used to produce detailed mineralogical maps of drill core. They suggest that the concepts and analytical techniqu...

Hyperspectral interactions: invariance and scaling

We present new invariance and scaling results for scale space analysis of hyperspectral data. First, we note that a hyperspectral curve can be segmented into independent regions selected by features of scale space fingerprints. These fingerprint features are persistent inflection points that precisely locate major atmospheric features that define the regions. The strength and location of hyperspectral features in one atmospheric region are independent of features in other regions; as a result, hyperspectral analysis can be simplified to a region-by-region analysis. We then generate simple scaling and invariance rules for features within such a spectral region. We show that the scale of individual features is independent of the details of feature shape and depends only on the area of the feature. Interacting features in turn exhibit a fascinating bifurcation behavior: at large separations features behave independently; at smaller separations features interact and their scales are damped; below a critical separation distance (the bifurcation point) the features nest. The scales of features above the bifurcation point, the scales of the nested features, and the location of the bifurcation point depend only on the feature areas and not on shape-associated parameters of the individual features.