Principal Component Images Research Articles

Improved Model for Starch Prediction in Potato by the Fusion of Near-Infrared Spectral and Textural Data.

In this study, visible-near-infrared (VIS-NIR) hyperspectral imaging was combined with a data fusion strategy for the nondestructive assessment of the starch content in intact potatoes. Spectral and textural data were extracted from hyperspectral images and transformed principal component (PC) images, respectively, and a partial least squares regression (PLSR) prediction model was then established. The results revealed that low-level data fusion could not improve accuracy in predicting starch content. Therefore, to improve prediction accuracy, key variables were selected from the spectral and textural data through competitive adaptive reweighted sampling (CARS) and correlation analysis, respectively, and mid-level data fusion was performed. With a residual predictive deviation (RPD) value > 2, the established PLSR model achieved satisfactory prediction accuracy. Therefore, this study demonstrated that appropriate data fusion can effectively improve the prediction accuracy for starch content and thus aid the sorting of potato starch content in the production line.

Multi-frame, ultrafast, x-ray microscope for imaging shockwave dynamics.

Inertial confinement fusion (ICF) holds increasing promise as a potential source of abundant, clean energy, but has been impeded by defects such as micro-voids in the ablator layer of the fuel capsules. It is critical to understand how these micro-voids interact with the laser-driven shock waves that compress the fuel pellet. At the Matter in Extreme Conditions (MEC) instrument at the Linac Coherent Light Source (LCLS), we utilized an x-ray pulse train with ns separation, an x-ray microscope, and an ultrafast x-ray imaging (UXI) detector to image shock wave interactions with micro-voids. To minimize the high- and low-frequency variations of the captured images, we incorporated principal component analysis (PCA) and image alignment for flat-field correction. After applying these techniques we generated phase and attenuation maps from a 2D hydrodynamic radiation code (xRAGE), which were used to simulate XPCI images that we qualitatively compare with experimental images, providing a one-to-one comparison for benchmarking material performance. Moreover, we implement a transport-of-intensity (TIE) based method to obtain the average projected mass density (areal density) of our experimental images, yielding insight into how defect-bearing ablator materials alter microstructural feature evolution, material compression, and shock wave propagation on ICF-relevant time scales.

Investigation of the data fusion of spectral and textural data from hyperspectral imaging for the near geographical origin discrimination of wolfberries using 2D-CNN algorithms

Deep convolutional neural networks have been applied to hyperspectral imaging (HSI) and have significantly improved modelling performance in many spectral analysis tasks due to their automatic extraction of relevant features. Using visible and near infrared hyperspectral (Vis-NIR) data, two-dimensional convolutional neural network (2D-CNN) discrimination models between the spectra of wolfberries and their corresponding classes of geographical origins were established and optimized using various variable selection and data fusion methods. The interval variable iterative space shrinking analysis (iVISSA), the uninformative variable elimination (UVE) algorithm, competitive adaptive reweighted sampling (CARS) and the iterative retained information variable (IRIV) algorithms were used to extract the feature wavelengths and compare the modelling effects; and then the 72 optimal wavelengths were extracted by the iVISSA algorithm. To extract the textural features of images, grey-level co-occurrence matrix (GLCM) analysis was conducted on the first principal component image. Models using variable selection methods based on low-level fusion data were superior to the corresponding methods based on single spectral data. The model based on iVISSA achieved the best result on mid-level fusion, the prediction set accuracy and mean F1 were 97.34% and 100%, respectively. Finally, optimized models of spectral-textural data were employed to identify the geographical origins of wolfberries. In general, the results showed that 2D-CNN model combined with fusion data of spectral and textural information can obtain excellent identification effect for the near geographical origins of wolfberries. This study may help develop an online detection system of near geographical origins of wolfberries.

Spectrum classification of citrus tissues infected by fungi and multispectral image identification of early rotten oranges

Citrus fruit is susceptible to postharvest rot by fungal infection. The detection of early rot is difficult due to similar skin characteristics to sound area, which limits the ability of the grading system to evaluate the comprehensive quality of citrus. In this study, the visible and near infrared hyperspectral imaging system with the wavelength range of 325–1000 nm was used to collect hyperspectral images of oranges. Hyperspectral data of three types of tissues including sound tissue from 80 samples, rotten tissue infected by Penicillium digitatum from 100 samples and rotten tissue infected by Penicillium italicum from 100 samples were extracted. The bootstrapping soft shrinkage (BOSS) and BOSS-SPA (BOSS-Successive Projections Algorithm) combination algorithm were separately used to optimize spectrum variables. The partial least squares discriminant analysis (PLS-DA) model for classifying three types of tissues and PLS-DA model for classifying two types of tissues (sound tissue and rotten tissue) were constructed based on full-spectrum and the selected informative variables. Model comparisonshowed that the BOSS-PLS-DA model can effectively identify three types of tissues with the classification accuracy of 97.1%, while the BOSS-SPA-PLS-DA model was more effective for the binary classification of sound and rotten citrus tissues with the accuracy of 100%. Furthermore, the wavelength images corresponding to the nine informative variables extracted by BOSS-SPA were performed the principal component analysis (PCA), and four feature wavelength images (508, 568, 578 and 614 nm) were obtained by analyzing the weighting coefficients of each single-wavelength images constituting the optimal principal component (PC) image. Finally, a fast multispectral image processing algorithm combined with the global threshold theory was proposed for the rotten orange detection based on the extracted four wavelength images. A total of 280 samples including 80 sound and 200 rotten samples were used to evaluate the classification ability, which showed the proposed multispectral image detection algorithm can successfully differentiate between sound and rotten oranges with an overall classification accuracy of 98.6%.

Two-wavelength image detection of early decayed oranges by coupling spectral classification with image processing

Citrus decay is one of the most serious postharvest diseases, which can cause great economic losses and food safety problem. Early decay has no obvious features, which makes rapid detection and grading of decayed citrus fruit a major challenge for citrus industry. This study utilized the unique image-spectrum fusion property of hyperspectral imaging, and proposed a strategy to quickly identify the citrus with early decay using only two wavelength images. The typical average spectra of sound and decayed tissues were extracted. The linear partial least squares-discriminant analysis (PLS-DA) and nonlinear back-propagation artificial neural network (BP-ANN) models were constructed for classifying two types of tissues. MC-UVE-SPA algorithm by combining Monte Carlo cross-validation (MC-UVE) with successive projections algorithm (SPA) was used to extract 16 variables characterizing two types of tissues. The wavelength images corresponding to the extracted variables were performed principal component analysis (PCA) to find the optimal PC image. Only two wavelength images at 568.8 nm and 771.2 nm were selected by analyzing the weighting coefficients of the third principal component (PC3) image. An improved watershed segmentation method was proposed to segment decay region in oranges based on PC2 image of the selected two wavelength images. Classification performance of the proposed algorithm was evaluated by all samples. The results showed that the overall classification accuracy of 96.6% was achieved, with 100% and 91.3% for the decayed and sound oranges respectively. The proposed detection strategy only involves two wavelength images, which will contribute to the establishment of a fast and low-cost multispectral imaging system for detection of citrus with early decay.

Lineament mapping in the Central High Atlas using ASTER and ASTER-GDEM data, (Morocco)

The present work focuses on lineament mapping using new spatial remote sensing techniques in the central High Atlas (CHA) of Morocco. In this study, the automatic extraction of lineaments was based on using multispectral images type Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER, L1B) with high spatial resolution up to 15 m for the visible/near-infrared (VNIR) bands. Besides, a digital elevation model (DEM) type ASTER GDEM was used with a spatial resolution of 30 m. The satellite image was pre-processed before the automatic extraction of the lineaments using the mathematical algorithm Line Module of the PCI Geomatica software 2016. Then, multisource data, such as an extract of the Ouarzazate Alougoum geological map (1/100000) covering the entire study area and a calibrated field imaging background (source: Esri, Maxar, GeoEye, Earthstar Geographics, CNES/Airbus, DS, USDA, AeroGRID, IGN, and the GIS User Community), was used for validating the results. The extraction of the lineaments from the first principal component (PC1) image was derived from the satellite image and the DEM. These results proved that each produced map has a strong similarity in concentration and orientation with three major directions including NE-SW, E-W and ENE-WSW. We observed a high concentration of these lineaments in both the central and northern parts of the study area as well.

Early diagnosis and pathogenesis monitoring of wheat powdery mildew caused by blumeria graminis using hyperspectral imaging

Powdery mildew caused by blumeria graminis is responsible for wheat yield losses in combination with a decline in quality. Hyperspectral imaging as a promising non-invasive sensor technique has potential for early diagnosis and pathogenesis monitoring of wheat powdery mildew, which is a practice that allows for precision crop protection. Hyperspectral images were first captured before inoculation as healthy samples and daily 2 to 5 days after inoculation (dai) as infected ones. Principal component analysis (PCA) was applied to observe the discrimination capability between samples at different infected stages, while a gray-level co-occurrence matrix (GLCM) was used to extract textural features from the first three principal component images. Then partial least squares discriminant analysis (PLS-DA) model was developed to evaluate the ability for early diagnosis of the disease using effective wavelengths, texture features and their fusion, respectively. Compared with the models using spectral or textural feature alone, PLS-DA model using the fused dataset obtained the best performances with classification accuracy of 91.4 % in validation sets. Furthermore, spectral angle mapping (SAM) was performed to identify the infected tissue in wheat leaves 2 dai, and to monitor the pathogenesis of powdery mildew over time. The results from this study could be used to develop a portable field monitoring sensor for wheat powdery mildew.

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial changes due to natural phenomena and/or human-induced activities. Mango is a major fruit crop in India, but detection of changes in mango crops remains a challenging task because the reason that many perennial crops have similar reflectance profiles. Therefore, a potent change detection technique is required for different applications such as the rate of deforestation, urban developments, damage evaluation, and resource monitoring. Compared to annual and seasonal crops, relatively few studies have been conducted on change detection in perennial fruit crops. In this study, a novel change detection technique (i.e., LR-PCA) is developed using the fusion of log-ratio (LR) and principal component analysis (PCA) images derived from bi-temporal soil adjusted vegetation index (SAVI) images to extract meaningful information and detect temporal changes in the mango fruit crop areas with high change detection accuracy. The proposed approach comprised two steps: (1) SAVI images from 2015 and 2019 were used to retrieve the log-ratio (LR) and principal component (PC) images, respectively, and both the images were fused by applying the pixel-by-pixel fusion approach. (2) Fused images were classified into three classes: “positive change”, “no change”, and “negative change” using a derived threshold value. The results show that the LR-PCA method of change detection yields a high change detection accuracy of 92% in comparison with the other change detection methods viz. vegetation image differencing, image ratioing, PCA, and log-ratio. To validate the adaptability of the proposed algorithm, experiments with two sets of bi-temporal SAVI indices images belonging to the Sitapur district of Uttar Pradesh State determine that the proposed change detection method performs well as compared to the existing individual methods for detection of changes in mango fruit crop. The proposed method is expected to be useful for detecting changes in the area of perennial crops. In the future, an accurate and efficient change detection analysis may be helpful for developing a real-time mango fruit crop monitoring system at the national level.

Water column compensation workflow for hyperspectral imaging data

Our article describes a data processing workflow for hyperspectral imaging data to compensate for the water column in shallow, clear to moderate optical water types. We provide a MATLAB script that can be readily used to implement the described workflow. We break down each code segment of this script so that it is more approachable for use and modification by end users and data providers. The workflow initially implements the method for water column compensation described in Lyzenga (1978) and Lyzenga (1981), generating depth invariant indices from spectral band pairs. Given the high dimensionality of hyperspectral imaging data, an overwhelming number of depth invariant indices are generated in the workflow. As such, a correlation based feature selection methodology is applied to remove redundant depth invariant indices. In a post-processing step, a principal component transformation is applied, extracting features that account for a substantial amount of the variance from the non-redundant depth invariant indices while reducing dimensionality. To fully showcase the developed methodology and its potential for extracting bottom type information, we provide an example output of the water column compensation workflow using hyperspectral imaging data collected over the coast of Philpott's Island in Long Sault Parkway provincial park, Ontario, Canada.•Workflow calculates depth invariant indices for hyperspectral imaging data to compensate for the water column in shallow, clear to moderate optical water types.•The applied principal component transformation generates features that account for a substantial amount of the variance from the depth invariant indices while reducing dimensionality.•The output (both depth invariant index image and principal component image) allows for the analysis of bottom type in shallow, clear to moderate optical water types.

Land use and land cover change detection using satellite remote sensing techniques in a tropical basin

In this investigation, the land use and land cover change detection are applied on a tropical basin, 10 change detection methods have been evaluated; nine of these are classified like a methods based on pixels and the last corresponds to the classified object method. Eleven images were acquired from the Landsat satellite in the period between 1986 and 2016. The percentages of change area according to each change detection method of pre-classification were: (a) Image difference: 7%–10%, (b) Image ratioing: 0.5%–3%, (c) NDVI image difference: 1%–4%, and (d) Principal component image difference: 4%–10%. The post-classification methods contributed with the pre-classification methods in a better approximation to the area difference proportion associated to each land use/land cover occurred in the study zone. Among the post-classification methods, it was found that the support vector machine provide results more approximate between these and their accuracy indexes are upper than those obtained through the maximum likelihood algorithm.

MURAL LINEAR FEATURE ENHANCEMENT ALGORITHM COMBINING SEMI-SUPERVISED IMAGE SEGMENTATION AND DIMENSIONALITY REDUCTION

Abstract. Due to the influence of natural and human factors, the linear features in the murals are partially blurred, which brings great challenges to the digital preservation and virtual restoration of cultural heritage. Taking the advantages of non-invasive measurement as well as the rich image and spectral information of hyperspectral technology, we proposed a linear feature enhancement method by combining semi-supervised superpixel segmentation with block dimension reduction. The main research work includes: (1) The true color composite image was segmented to obtain the label data by using the local spatial information of the superpixel image and the global feature information extracted by fuzzy c-means (FCM) clustering.(2) According to the label data, the preprocessed hyperspectral data were divided into homogeneous regions, whose dimensionality was reduced by principal component analysis (PCA) and kernel principal component analysis (KPCA). (3) The principal component images with the largest gradient after dimensionality reduction were respectively selected and normalized. The optimal principal component images normalized by the block PCA and block KPCA dimensionality reduction algorithms are superimposed to produce the linear feature enhancement images of murals. The hyperspectral images of some murals in Qutan Temple, Qinghai Province, China were used to verify the method. The results show that the spatial information and the spectral information of different pattern areas in the hyperspectral image can be fully used by combining the superpixel FCM image segmentation algorithm with the dimensionality reduction algorithm. of. It can highlight the linear information in the hyperspectral images of fades murals.

Multi-Scale Superpixels Dimension Reduction Hyperspectral Image Classification Algorithm Based on Low Rank Sparse Representation Joint Hierarchical Recursive Filtering.

The original Hyperspectral image (HSI) has different degrees of Hughes phenomenon and mixed noise, leading to the decline of classification accuracy. To make full use of the spatial-spectral joint information of HSI and improve the classification accuracy, a novel dual feature extraction framework joint transform domain-spatial domain filtering based on multi-scale-superpixel-dimensionality reduction (LRS-HRFMSuperPCA) is proposed. Our framework uses the low-rank structure and sparse representation of HSI to repair the unobserved part of the original HSI caused by noise and then denoises it through a block-matching 3D algorithm. Next, the dimension of the reconstructed HSI is reduced by principal component analysis (PCA), and the dimensions of the reduced images are segmented by multi-scale entropy rate superpixels. All the principal component images with superpixels are projected into the reconstructed HSI in parallel. Secondly, PCA is once again used to reduce the dimension of all HSIs with superpixels in scale with hyperpixels. Moreover, hierarchical domain transform recursive filtering is utilized to obtain the feature images; ultimately, the decision fusion strategy based on a support vector machine (SVM) is used for classification. According to the Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient on the three datasets (Indian Pines, University of Pavia and Salinas), the experimental results have shown that our proposed method outperforms other state-of-the-art methods. The conclusion is that LRS-HRFMSuperPCA can denoise and reconstruct the original HSI and then extract the space-spectrum joint information fully.

Soluble solids content monitoring for shelf-life assessment of table grapes coated with chitosan using hyperspectral imaging

Table grapes have a relatively short shelf-life and degradation rate due to pathogen infestation during postharvest storage. Chitosan (CTS) edible coatings offer a potential alternative for extending the shelf-life of fruit. The objective of this study was to explore the efficacy of CTS edible coatings on the shelf-life of table grapes by the application of hyperspectral imaging for non-invasive and rapid soluble solids content (SSC) monitoring. Table grapes were coated with CTS solutions (0.5%, and 1%, w/v), while the uncoated fruit served as controls. Spectral changes over storage days were investigated using principal component analysis (PCA), and principal component images were extracted to visualize the degradation evolution. Table grapes coated with edible coatings presented significant delays in SSC change and degradation process compared with uncoated fruit. Moreover, partial least squares regression (PLSR) combined with feature bands selected by competitive adaptive reweight sampling (CARS) provided the better performance for SSC prediction with R2 higher than 0.801 for the uncoated and coated grapes. Conclusively, hyperspectral imaging was capable of monitoring the evolution of SSC, and 1% CTS coating was more effective in extending the shelf-life and maintaining postharvest quality of table grapes.

Mapping hydrothermal alteration mineral deposits from Landsat 8 satellite data in Pala, Mayo Kebbi Region, Southwestern Chad

The different image attribute extracting techniques, spectral enhancement ability, pixel classification methods have been applied on the Landsat 8 OLI satellite image data to map geological features and spatial distribution of hydrothermal alteration zones, which have been recorded to be associated with gold mineralization in the Pala, Mayo Kebbi region of southwestern Chad. Radiometric calibrated, noise filtered and image enhanced Landsat 8 OLI data were subjected to colour composite, band rationing and crosta analyses in order to enhance spectral reflectance and compositional information, create colourful multispectral images which enable the mapping of desired spectral bands into principal component images for the purpose of accentuating salient information needful to delineate hydrothermal alteration zones within the study area.Composite ratios 4/2-6/7-6/5 indicate relative spatial distribution of iron-oxide in the southeast while clays and hydroxyl minerals occur in the west and northwestern part of the study area. The eigenvectors and eigenvalues for hydroxyl minerals from the crosta analysis indicate highest loadings of opposite signs (-0.721498 & 0.649465; bands 6 & 7) in PC3, representing ‘’H’’ image. The eigenvectors and eigenvalues for iron-oxides on the other hand present highest loading of opposite signs (0.895721 & -0.443283; bands 2 & 4) in PC4, representing the “F” image. The white coloured pixels within the bright reflections generated from Crosta composite image of H, H+F & F in RGB delineate hydrothermal alteration zones. The plot of delineated zones of hydrothermal alteration on the Landsat 8 true colour indicate spatial distribution of the alteration areas within the host greenstone belt belonging to the Precambrian Goueygoudoum series and they plot mainly along major structural features, suggesting structural control. Field control revealed that some areas delineated by band rationing as hydrothermal alteration zones appear to be the result of intense weathering. Accordingly, we recommend that remote sensing delineation of hydrothermal alteration zones should be checked by a relevant ground-observation based control procedure.

Data Hiding Method with Principal Component Analysis and Image Coordinate Conversion

Data hiding method with Principal Component Analysis (PCA) and image coordinate conversion as a preprocessing of wavelet Multi Resolution Analysis (MRA) is proposed. The method introduced in this paper, based on the characteristics of the original multispectral image, allows recovering the secret data. Through experiments, it is found that the proposed method is superior to the conventional data hiding method without any preprocessing. The method introduced in this paper allows only I who knows the characteristics of the original multispectral image to recover the secret data, i.e., when the information of the original image needs to be protected. Moreover, in the introduced method, the information of the secret data is protected by the existence of the eigenvector and the oblique coordinate transformation, that is, the secret data is restored if at least the information of the true original image is not known. The principal component transformation coefficient differs for each original image and is composed of the eigenvectors of the original image.

Impruved Prediction of Soluble Solid Content of Apple Using a Combination of Spectral and Textural Features of Hyperspectral Images

We established prediction models based on the combination of spectral and different advanced image features to improve the prediction accuracy of solid-soluble content (SSC) of apple. Eight optimal wavelengths were selected using a new variable selection method called variable combination population analysis (VCPA). Image textural features of the first three principal component score images were obtained using a gray level co-occurrence matrix (GLCM) and a local binary pattern (LBP). Next, a random frog algorithm was developed to select optimal textural features for further analysis. A support vector regression (SVR) model based on spectral and different textural features was developed to predict the SSC of the apple. The model based on eight optimal wavelengths and nine optimal GLCM features of principal component images yielded the best result with the determination coefficient for prediction (Rp2) of 0.9193, root mean square error for prediction (RMSEP) of 0.2955, and the ratio of the standard deviation of the prediction set to the root mean square error of prediction (RPD) with a value of 3.50. These results revealed that the spectral combined with optimal GLCM features from principal component images coupled with the SVR model has the potential for prediction of the SSC of apple.

Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening.

With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency band. However, due to the limitations of current voice equipment and channel conditions, the existing voice band expansion technology often ignores the high-frequency and low-frequency correlation of the audio, resulting in excessive smoothing of the recovered high-frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA-NN (principal components analysis-neural network) based on principal component image analysis is proposed. Based on the nonlinear characteristics of the audio image signal, the model reduces the dimension of high-dimensional data and realizes the effective recovery of the high-frequency detailed spectrum of audio signal in phase space. The results show that the PCA-NN, i.e., neural network based on principal component analysis, is superior to other audio expansion algorithms in subjective and objective evaluation; in log spectrum distortion evaluation, PCA-NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286 dB, 0.51 dB, and 0.15 dB, respectively. The above results show that in the image frequency band expansion of college English listening, the neural network algorithm based on principal component analysis (PCA-NN) can obtain better high-frequency reconstruction accuracy and effectively improve the audio quality.

Study on the Evolution Monitoring of Topographic and Hydrological Characteristics of Small Watershed Based on Remote Sensing and GIS

Soil and water conservation is one of the key measures to improve the ecological environment of small watersheds and maintain the healthy life of the region. With the current method used to monitor the evolution of topographic and hydrological characteristics of a small watershed, the edge keeping index of the image and the signal-to-noise ratio of the image are low, the monitoring results are inaccurate, and there are some problems such as low edge keeping ability, poor denoising effect, and low monitoring accuracy. A monitoring method based on remote sensing and GIS for the evolution of topographic and hydrological characteristics of small watersheds is proposed. The hyperspectral data was transformed by the Principal Component Analysis (PCA). Group principal component images, use sparse representation method based on adaptive dictionary and dual-tree complex wavelet transform method to denoise principal component images with a small amount of information were the objectives of the paper; also the use the multi-scale wavelet transform to detect image edge; build a binary model of a pixel, extract vegetation index, and terrain factor based on a binary model of the pixel, and realize terrain and hydrological characteristics of the small watershed. The experimental results show that the proposed method has the high edge-preserving ability, good denoising effect, and high monitoring accuracy.

Behind the Scene of "The Holy Family with St. Anne and the Young St. John" by Bernardino Luini: A Computer-Assisted Method to Unveil the Underdrawings.

Uncovering the underdrawings (UDs), the preliminary sketch made by the painter on the grounded preparatory support, is a keystone for understanding the painting's history including the original project of the artist, the pentimenti (an underlying image in a painting providing evidence of revision by the artist) or the possible presence of co-workers' contributions. The application of infrared reflectography (IRR) has made the dream of discovering the UDs come true: since its introduction, there has been a growing interest in the technology, which therefore has evolved leading to advanced instruments. Most of the literature either report on the technological advances in IRR devices or present case studies, but a straightforward method to improve the visibility of the UDs has not been presented yet. Most of the data handling methods are devoted to a specific painting or they are not user-friendly enough to be applied by non-specialized users, hampering, thus, their widespread application in areas other than the scientific one, e.g., in the art history field. We developed a computer-assisted method, based on principal component analysis (PCA) and image processing, to enhance the visibility of UDs and to support the art-historians and curators' work. Based on ImageJ/Fiji, one of the most widespread image analysis software, the algorithm is very easy to use and, in principle, can be applied to any multi- or hyper-spectral image data set. In the present paper, after describing the method, we accurately present the extraction of the UD for the panel "The Holy Family with St. Anne and the Young St. John" and for other four paintings by Luini and his workshop paying particular attention to the painting known as "The Child with the Lamb".

Reflectance spectroscopy-guided broadband spectral derivative approach to detect glauconite-rich zones in fossiliferous limestone, Kachchh region, Gujarat, India

In this study, we processed visible near-infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to delineate glauconite-bearing zones in fossiliferous limestones, Quaternarysediments and Paleogene shales in the Lakhpat.Region of the Kachchh Province in India. Glauconitic limestone has subtle absorption features at 2.3 µm when compared to the absorption features of pure limestones that are dominated by the strong absorption feature of calcite at 2.33 µm. Additionally, the spectrum of glauconite-rich limestone also has an absorption feature at about 0.9 µm due to the crystal field effect of Fe2+ present in the glauconite structure and a feeble spectral feature due to the presence of Al-OH bonds at about 2.2 µm. ASTER resampled reflectance spectra of these rocks have been used as the reference to construct different image-enhanced products. We derived index images and Eigen matrix analysis-based principal components (PCs), using ASTER bands, to delineate different rocks in the study area. These index images are derived to enhance broadband absorption features observed in shales, glauconite-bearing and pure limestones, respectively. Principal component images are prepared using the spectral bands defining the broadband absorption features of these rocks. We found that the PC image composite, derived using PC 8, PC 2 and PC 7, is suitable to discriminate basalts, limestones and shales from each other, while an index image colour composite, prepared using three independent index images, is suitable for the identification of lithological contacts between Quaternary sediments, shales and fossiliferous limestones, respectively. In addition, the ASTER resampled rock spectrum of glauconite has a relatively lower spectral gradient in comparison to the gradient observed in the spectrum of fossiliferous limestone within the spectral range of bands 6 and 8. The spectral derivative images are derived for discriminating glauconite-rich zones within the limestone using the contrast in the spectral gradient of limestone and glauconite-rich limestone. The image composite of three spectral derivatives was compiled from the derivative image of bands 4 to 3, bands 5 to 7, and bands 6 to 8 to discriminate glauconite-rich zones from pure fossiliferous limestone. Our study proposes the potential use of broadband reflectance spectroscopy in the mineral mapping of sedimentary provinces.