Extraction Of Spectral Information Research Articles

Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS.

MULTISPECTRAL AND HYPERSPECTRAL SATELLITE DATA FOR ALTERATION MAPPING USING PARTIAL-SUBPIXEL ALGORITHM IN AN UNEXPLORED REGION

Abstract. The focus of this paper is to comparatively evaluate the performance of ASTER and Hyperion data for target detection of hydrothermal alteration zones associated with geothermal (GT) springs in an unexplored savannah region. The study employed the partial subpixel unmixing Mixture Tuned Match Filtering algorithm for spectral information extraction using the multispectral and hyperspectral satellite data. In both cases, image endmember spectra specifically for kaolinite, alunite, and illite and calcite zones were selected and extracted by using the Analytical Imaging and Geophysics (AIG)-developed processing methods. The results show that the Hyperion, despite its distortions, can effectively discriminate associated alteration zones better than ASTER. Consequently, Hyperspectral data and analysis are thus recommended for use in similar unexplored regions for GT resource detection and monitoring.

Spatial scanning hyperspectral imaging combining a rotating slit with a Dove prism.

We present a new concept for spatial scanning hyperspectral imaging. Spatial scanning is one of the main methods used for hyperspectral data acquisition and can provide high spectral resolution over a wide spectral range. However, conventional techniques, such as the whiskbroom and the pushbroom techniques, suffer from the need for relative motion between the target and the imaging system, which increases the complexity on the hardware side and limits the application possibilities. Our new approach combines a rotating slit and a co-rotating Dove prism. The rotating slit scans the target image by selecting one line from the image at each angular position of the slit. The rotating Dove prism is used to synchronously re-align the transmitted light from the selected image line with respect to the transmission grating to allow the projection of the diffracted light over the same range of pixel columns of the image sensor to facilitate data acquisition and extraction of spectral information. The new approach enables the spatial scanning of the target image without the need for relative linear motion or the use of additional external equipment and therefore opens the door for more application scenarios.

Spectral analysis framework for compressed sensing ultrasound signals.

Compressed sensing (CS) is the theory of the recovery of signals that are sampled below the Nyquist sampling rate. We propose a spectral analysis framework for CS data that does not require full reconstruction for extracting frequency characteristics of signals by an appropriate basis matrix. The coefficients of a basis matrix already contain the spectral information for CS data, and the proposed framework directly utilizes them without completely restoring original data. We apply three basis matrices, i.e., DCT, DFT, and DWT, for sampling and reconstructing processes, subsequently estimating the attenuation coefficients to validate the proposed method. The estimation accuracy and precision, as well as the execution time, are compared using the reference phantom method (RPM). The experiment results show the effective extraction of spectral information from CS signals by the proposed framework, and the DCT basis matrix provides the most accurate results while minimizing estimation variances. The execution time is also reduced compared with that of the traditional approach, which completely reconstructs the original data. The proposed method provides accurate spectral analysis without full reconstruction. Since it effectively utilizes the data storage and reduces the processing time, it could be applied to small and portable ultrasound systems using the CS technique.

Evaluation and Detection of Geothermal Potential Zones in Yankari Park, South- Central Part of Bauchi State, North-Eastern Nigeria

Exploiting geothermal (GT) resources requires first and foremost locating suitable areas for its development. The focus of this paper is to explore the applicability of mapping GT related anomalies by identifying mineralogy and lithological features at regional scale using image enhancement techniques on Landsat 8 satellite data. A combination of established Band ratios from literature were implemented on Landsat 8 optical bands using innovative digital image processing and knowledge-based spectral information extraction techniques which enhanced surface mineralogy and possible alteration areas of interest. The results indicate that ratios using bands; 7/4, 6/3, 5/7 and 6/7, 4/3, 5/6 and 7/5, 5/4, 6/7 respectively as RGB, were unique but corroborative in enhancing and identifying; clay rich rocks, iron oxides, micas, carbonates and possible hydrothermal alteration zones related to GT anomaly. The results could have implication for GT exploration in especially unexplored regions where expensive airborne surveys are unaffordable.Keywords: Band ratios, Landsat 8 (OLI), Geothermal, Minerals

Fully connected neural network for virtual monochromatic imaging in spectral computed tomography.

Spectral computed tomography (SCT) has advantages in multienergy material decomposition for material discrimination and quantitative image reconstruction. However, due to the nonideal physical effects of photon counting detectors, including charge sharing, pulse pileup and -escape, it is difficult to obtain precise system models in practical SCT systems. Serious spectral distortion is unavoidable, which introduces error into the decomposition model and affects material decomposition accuracy. Recently, neural networks demonstrated great potential in image segmentation, object detection, natural language processing, etc. By adjusting the interconnection relationship among internal nodes, it provides a way to mine information from data. Considering the difficulty in modeling SCT system spectra and the superiority of data-driven characteristics of neural networks, we proposed a spectral information extraction method for virtual monochromatic attenuation maps using a simple fully connected neural network without knowing spectral information. In our method, virtual monochromatic linear attenuation coefficients can be obtained directly through our neural network, which could contribute to further material recognition. Our method also provides outstanding performance on denoising and artifacts suppression. It can be furnished for SCT systems with different settings of energy bins or thresholds. Various substances available can be used for training. The trained neural network has a good generalization ability according to our results. The testing mean square errors are about .

ASTER Spectral Analysis for Host Rock Associated with Porphyry Copper-molybdenum Mineralization

ABSTRACT This study is an attempt to understand the application of spectral image processing methods to ASTER data for mapping host rock associated with porphyry copper-molybdenum mineralization. The application of remote sensing in identifying variations in surface mineralogy, structural elements, and lithologic contacts can help in identifying such relations. Signatures collected from the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) data is used to map the host rock of Zafarqand area. The study area is located in the central segment of the Urumieh–Dokhtar volcanic belt of Iran. The Urumieh-Dokhtar is a potential zone for exploration of new porphyry copper deposits. Band ratio, band math, false colour composites and principal component analysis techniques are used to refine the different lithologic units in the area. These methods showed the discrimination of acidic igneous rock from intermediate igneous rock and the boundary between igneous rocks using the shortwave infrared bands (SWIR) of ASTER at regional scale. Results have proven to be effective and in accordance with the results of field observations, ore microscopy examination and with reference geological map. In conclusion, the image processing methods used can provide cost-effective information to discover possible locations of porphyry copper and epithermal gold mineralization prior to detailed and costly ground investigations. The extraction of spectral information from ASTER data can produce comprehensive and accurate information for copper and gold resource investigations around the world, including those yet to be discovered.

Highly selective vapochromic fluorescence of polycarbonate films Doped with an ICT‐Based solvatochromic probe

ABSTRACTThe vapochromic properties of a fluorescent 3‐[2‐(4‐nitrophenyl) ethenyl]‐1‐(2‐ethylhexyl)‐2‐methylindole (NPEMI‐E) characterized by intramolecular charge transfer (ICT) character, dispersed in polycarbonate (PC) films is reported. NPEMI‐E solvatochromism is investigated by means of experimental and computational methods. Fluorescent PC films containing 0.1 wt % of NPEMI‐E are prepared and exposed to saturated atmospheres of different volatile organic compounds (VOCs). NPEMI‐E/PC films show remarkable and reversible vapochromism when exposed to VOCs with high polarity index and favorable interaction with PC matrix such as CHCl3. Only minor variations in the emission wavelength are actually recorded for all other classes of VOCs investigated. The hue parameter is also used for the effective extraction of spectral information from digital color images without the need for wavelength discriminators. Overall, the present results support the use of NPEMI‐E/PC films for the cost‐effective detection of CHCl3 vapors. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2017, 55, 1171–1180

Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products

Hyperspectral imaging is built with the aggregation of imaging, spectroscopy and radiometric techniques. This technique observes the sample behaviour when it is exposed to light and interprets the properties of the biological samples. As hyperspectral imaging helps in interpreting the sample at the molecular level, it can distinguish very minute changes in the sample composition from its scatter properties. Hyperspectral data collection depends on several parameters such as electromagnetic spectrum wavelength range, imaging mode and imaging system. Spectral data acquired using a hyperspectral imaging system contain variations due to external factors and imaging components. Moreover, food samples are complex matrices with conditions of surface and internal heterogeneities, which may lead to variations in acquired data. Hence, before extracting information, these variations and noises must be reduced from the data using reference-dependent or reference-independent spectral pre-processing techniques. Using of the entire hyperspectral data for information extraction is tedious and time-consuming. In order to overcome this, exploratory data analysis techniques are used to select crucial wavelengths from the excessive hyperspectral data. Using appropriate chemometric techniques (supervised or unsupervised learning techniques) on this pre-processed hyperspectral data, qualitative or quantitative information from sample can be obtained. Qualitative information for analysing of the chemical composition, detecting of the defects and determining the purity of the food product can be extracted using discriminant analysis techniques. Quantitative information including variation in chemical constituents and contamination levels in food and agricultural sample can be extracted using categorical regression techniques. In combination with appropriate spectra pre-processing and chemometric technique, hyperspectral imaging stands out as an advanced quality evaluation system for food and agricultural products.

Doubling the spectrum of time-domain induced polarization by harmonic de-noising, drift correction, spike removal, tapered gating and data uncertainty estimation

The extraction of spectral information in the inversion process of time-domain (TD) induced polarization (IP) data is changing the use of the TDIP method. Data interpretation is evolving from a qualitative description of the subsurface, able only to discriminate the presence of contrasts in chargeability parameters, towards a quantitative analysis of the investigated media, which allows for detailed soil- and rock-type characterization. Two major limitations restrict the extraction of the spectral information of TDIP data in the field: i) the difficulty of acquiring reliable early-time measurements, in the millisecond range and ii) the self-potential drift in the measured potentials distorting the shape of the late time IP responses, in the second range. Recent developments in TDIP acquisition equipment have given access to full waveform recordings of measured potentials and transmitted current, opening a breakthrough for data processing. For measuring at early times, we developed a new method for removing the significant noise from powerlines contained in the data through a model-based approach, localizing the fundamental frequency of the powerline signal in the full-waveform IP recordings. By this, we cancel both the fundamental signal and its harmonics. Furthermore, a novel and efficient processing scheme for identifying and removing spikes TDIP data is developed. The noise cancellation and the de-spiking allow the use of earlier and narrower gates, down to a few milliseconds after the current turn-off. Furthermore, tapered windows are used in the final gating of IP data, allowing the use of wider and overlapping gates for higher noise suppression without signal distortion. For measuring at late times, we have developed an algorithm for removal of the self-potential drift. Usually constant or linear drift-removal algorithms are used, but these algorithms fail in removing the background potentials due to the polarization of the electrodes previously used for current injection. We developed a drift-removal scheme that model the polarization effect and efficiently allows for preserving the shape of the IP responses at late times. Uncertainty estimates are essential in the inversion of IP data. Therefore, in the final step of the data processing, we estimate the data standard deviation based on the data variability within the IP gates and the misfit of the background drift removal Overall, the removal of harmonic noise, spikes, self-potential drift, tapered windowing and the uncertainty estimation allows for doubling the usable range of TDIP data to almost four decades in time (corresponding to four responses in frequency), and will significantly advance the science and the applicability of the IP method. (Less)

A Novel Method for Spectral Similarity Measure by Fusing Shape and Amplitude Features

Spectral similarity measure is the basis of spectral information extraction. The description of spectral features is the key to spectral similarity measure. To express the spectral shape and amplitude features reasonably, this paper presents the definition of shape and amplitude feature vector, constructs the shape feature distance vector and amplitude feature distance vector, proposes the spectral similarity measure by fusing shape and amplitude features (SAF), and discloses the relationship of fusing SAF with Euclidean distance and spectral information divergence. Different measures were tested on the basis of United States Geological Survey (USGS) mineral_beckman_430. Generally, measures by integrating SAF achieve the highest accuracy, followed by measures based on shape features and measures based on amplitude features. In measures by integrating SAF, fusing SAF shows the highest accuracy. Fusing SAF expresses the measured results with the inner product of shape and amplitude feature distance vectors, which integrate spectral shape and amplitude features well. Fusing SAF is superior to other similarity measures that integrate SAF, such as spectral similarity scale, spectral pan-similarity measure, and normalized spectral similarity score(NS 3 ).

Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform

Power quality is main issue because of the impact to electricity suppliers, equipments, manufacturers and user.To solve the power quality problem, an analysis of power quality disturbances is required to identify and rectify any failures on power system. Most of researchers apply fourier transform in power quality analysis, however the ability of fourier transform is limited to spectral information extraction that can be applied on stationary disturbances. Thus, time-frequency analysis is introduced for analyzing the power quality distubances because of the limitation of fourier transform. This paper presents the analysis of real power quality disturbances using S-transform. This time-frequency distribution (TFD) is presented to analyze power quality disturbances in time-frequency representation (TFR). From the TFR, parameters of the disturbances such as instantaneous of root mean square (RMS), fundamental RMS, total harmonic distortion (THD), total nonharmonic distortion (TnHD) and total waveform distortion (TWD) of the disturbances are estimated. The experimental of three phase voltage inverter and starting motor are conducted in laboratory to record the real power quality disturbances. The disturbances are recorded via data logger system which is mplemented using LabVIEW while the analysis is done using Matlab in offline condition. The results show that S-transform gives good performance in identifying, detecting and analyzing the real power quality disturbances, effectively.

Processing changes when listening to foreign-accented speech.

This study investigates the mechanisms responsible for fast changes in processing foreign-accented speech. Event Related brain Potentials (ERPs) were obtained while native speakers of Spanish listened to native and foreign-accented speakers of Spanish. We observed a less positive P200 component for foreign-accented speech relative to native speech comprehension. This suggests that the extraction of spectral information and other important acoustic features was hampered during foreign-accented speech comprehension. However, the amplitude of the N400 component for foreign-accented speech comprehension decreased across the experiment, suggesting the use of a higher level, lexical mechanism. Furthermore, during native speech comprehension, semantic violations in the critical words elicited an N400 effect followed by a late positivity. During foreign-accented speech comprehension, semantic violations only elicited an N400 effect. Overall, our results suggest that, despite a lack of improvement in phonetic discrimination, native listeners experience changes at lexical-semantic levels of processing after brief exposure to foreign-accented speech. Moreover, these results suggest that lexical access, semantic integration and linguistic re-analysis processes are permeable to external factors, such as the accent of the speaker.

Backside spectroscopic photon emission microscopy using intensified silicon CCD

A typical solution for backside analysis of photon emissions from semiconductor integrated circuits has been the InGaAs detector. It takes advantage of the transparency of the silicon material to wavelengths within its spectral sensitivity regime. However, it has been recently demonstrated that light spectral information extraction is more reliable when using silicon CCDs (SiCCDs) assisted by proper backside bulk Si thinning of the device. In this paper we demonstrate further improvement of the photon emission spectrum acquisition using the solid-state intensified silicon CCD (SI-CCD). Using the presented solution it is possible to significantly reduce the analysis time, expand the detectable spectral regime as well as improve the spectral wavelength resolution.

ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration.

This paper provides a review of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and Hyperion data and applications of the data as a tool for ore minerals exploration, lithological and structural mapping. Spectral information extraction from ASTER, ALI, and Hyperion data has great ability to assist geologists in all disciplines to map the distribution and detect the rock units exposed at the earth’s surface. The near coincidence of Earth Observing System (EOS)/Terra and Earth Observing One (EO-1) platforms allows acquiring ASTER, ALI, and Hyperion imagery of the same ground areas, resulting accurate information for geological mapping applications especially in the reconnaissance stages of hydrothermal copper and gold exploration, chromite, magnetite, massive sulfide and uranium ore deposits, mineral components of soils and structural interpretation at both regional and district scales. Shortwave length infrared and thermal infrared bands of ASTER have sufficient spectral resolution to map fundamental absorptions of hydroxyl mineral groups and silica and carbonate minerals for regional mapping purposes. Ferric-iron bearing minerals can be discriminated using six unique wavelength bands of ALI spanning the visible and near infrared. Hyperion visible and near infrared bands (0.4 to 1.0 μm) and shortwave infrared bands (0.9 to 2.5 μm) allowed to produce image maps of iron oxide minerals, hydroxyl-bearing minerals, sulfates and carbonates in association with hydrothermal alteration assemblages, respectively. The techniques and achievements reviewed in the present paper can further introduce the efficacy of ASTER, ALI, and Hyperion data for future mineral and lithological mapping and exploration of the porphyry copper, epithermal gold, chromite, magnetite, massive sulfide and uranium ore deposits especially in arid and semi-arid territory.

Identifying areas of high economic-potential copper mineralization using ASTER data in the Urumieh–Dokhtar Volcanic Belt, Iran

This study investigates the application of spectral image processing methods to ASTER data for mapping hydrothermal alteration zones associated with porphyry copper mineralization and related host rock. The study area is located in the southeastern segment of the Urumieh–Dokhtar Volcanic Belt of Iran. This area has been selected because it is a potential zone for exploration of new porphyry copper deposits. Spectral transform approaches, namely principal component analysis, band ratio and minimum noise fraction were used for mapping hydrothermally altered rocks and lithological units at regional scale. Spectral mapping methods, including spectral angle mapper, linear spectral unmixing, matched filtering and mixture tuned matched filtering were applied to differentiate hydrothermal alteration zones associated with porphyry copper mineralization such as phyllic, argillic and propylitic mineral assemblages. Spectral transform methods enhanced hydrothermally altered rocks associated with the known porphyry copper deposits and new identified prospects using shortwave infrared (SWIR) bands of ASTER. These methods showed the discrimination of quartz rich igneous rocks from the magmatic background and the boundary between igneous and sedimentary rocks using the thermal infrared (TIR) bands of ASTER at regional scale. Spectral mapping methods distinguished the sericitically- and argillically-altered rocks (the phyllic and argillic alteration zones) that surrounded by discontinuous to extensive zones of propylitized rocks (the propylitic alteration zone) using SWIR bands of ASTER at both regional and district scales. Linear spectral unmixing method can be best suited for distinguishing specific high economic-potential hydrothermal alteration zone (the phyllic zone) and mineral assemblages using SWIR bands of ASTER. Results have proven to be effective, and in accordance with the results of field surveying, spectral reflectance measurements and X-ray diffraction (XRD) analysis. In conclusion, the image processing methods used can provide cost-effective information to discover possible locations of porphyry copper and epithermal gold mineralization prior to detailed and costly ground investigations. The extraction of spectral information from ASTER data can produce comprehensive and accurate information for copper and gold resource investigations around the world, including those yet to be discovered.

위상 보상과 가중치 평균을 이용한 의료 초음파 신호의 주파수 특성 추출 방법

Quantitative ultrasound analysis provides fundamental information of various ultrasound parameters using spectral information of the short-gated radiofrequency(RF) data. Therefore, accurate extraction of spectral information from backscattered RF signal is crucial for further analysis of medical ultrasound parameters. In this paper, we propose two techniques for calculating a more accurate power spectrum which are based on the phase-compensation using the normalized cross-correlation to minimize estimation errors due to phase variations, and the weighted averaging technique to maximize the signal-to-noise ratio(SNR). The simulation results demonstrate that the proposed method estimates better results with 10% smaller estimation variances compared to the conventional methods.

Polarization and phase pulse shaping applied to structural contrast in nonlinear microscopy imaging

The spectral polarization shaping of ultrashort pulses is shown to allow retrieval of two-dimensional individual tensorial components of the second-harmonic-generation response of molecular samples in nonlinear microscopy imaging. This configuration, which cannot be performed by traditional polarization-controlled excitation, provides a structural contrast that can be directly related to information on the local symmetry and order of the sample, with submicrometric spatial resolution. Phase shaping, in addition to polarization spectral manipulation, is proposed as a possible scheme for imaging individual tensorial components without the need for a spectral information extraction.

A novel power spectrum calculation method using phase-compensation and weighted averaging for the estimation of ultrasound attenuation

An estimation of ultrasound attenuation in soft tissues is critical in the quantitative ultrasound analysis since it is not only related to the estimations of other ultrasound parameters, such as speed of sound, integrated scatterers, or scatterer size, but also provides pathological information of the scanned tissue. However, estimation performances of ultrasound attenuation are intimately tied to the accurate extraction of spectral information from the backscattered radiofrequency (RF) signals. In this paper, we propose two novel techniques for calculating a block power spectrum from the backscattered ultrasound signals. These are based on the phase-compensation of each RF segment using the normalized cross-correlation to minimize estimation errors due to phase variations, and the weighted averaging technique to maximize the signal-to-noise ratio (SNR). The simulation results with uniform numerical phantoms demonstrate that the proposed method estimates local attenuation coefficients within 1.57% of the actual values while the conventional methods estimate those within 2.96%. The proposed method is especially effective when we deal with the signal reflected from the deeper depth where the SNR level is lower or when the gated window contains a small number of signal samples. Experimental results, performed at 5 MHz, were obtained with a one-dimensional 128 elements array, using the tissue-mimicking phantoms also show that the proposed method provides better estimation results (within 3.04% of the actual value) with smaller estimation variances compared to the conventional methods (within 5.93%) for all cases considered.

The Spiros imaging software for the Integral SPI spectrometer

A key tool in the package of software available for the analysis of data from the SPI spectrometer of Integral is the SPIROS system developed at the University of Birmingham. Although intended primarily for the analysis of point sources and for the extraction of spectral information, SPIROS has many additional capabilities. The software is described with particular emphasis on the most widely used modes of operation and on the relationship to other imaging and data reduction techniques.