Two dimensional spectral color analysis of colored materials utilizing computer image analyzing system.

The principal component analysis (PCA) and the non-negative matrix factorization (NNMF) methods are boosted for the recovery of the reflectance spectra from the corresponding CIEXYZ tristimulus values under a given set of viewing conditions by the estimation of CIEXYZ tristimulus values under the second set of illumination through the implementation of weighted regression technique. The weighting function is determined on the basis of the colorimetric differences of desired sample with the samples of testing set. The calculated weights are then used in a weighted regression procedure in an attempt for better estimation of CIEXYZ tristimulus values of samples under the second illuminant. In this manner, the tristimulus values of the training samples under two sets of viewing conditions become available. Two sets of bases, i.e., the six positive-negative eigenvectors and the six non-negative features of reflectance spectra data, are used for spectral recovery purpose. The suggested method increases the spectral and colorimetric performances of recovery.

Patch test has been used to determine the existance of metal allergy, and the severity thereof has been determined by the change in skin color.In order to obtain an accurate judgment of the change in skin color, the statistical formulation was calculated based on colorimetric investigation into 9 kinds of color characteristics: 1) Spectral reflectance curve, 2) Color difference, 3) Depth of shade, 4) CIE Lab-L*, 5) CIE Lab-a*, 6) CIE Lab-b*, 7) Munsell-H, 8) Munsell-C, and 9) Munsell-V.The rate of the agreement with statistical judgment and that of conventional subjective was 100% in positive reaction, and ranged from 90% to 94% in negative reaction.In conclusion, the original statistical formulation based on colorimetric characteristics showed high level accuracy for determining the change in skin color reaction by the patch test.

Abstract. Most algorithms used to extract information about objects in satellite images rely on data derived from groups of pixels (i.e. Training sites). The information space pertaining to individual pixels. However, remains largely underutilized. Here, we introduce a new approach to understanding pixel space by analyzing its shape descriptors derived from the spectral reflectance curve (SDSRC), and we illustrate an application of this concept. Six shape-based feature extraction and representation methods were explored. Among them, the Shape Center of Gravity (COG) or centroid, Area Under Curve (AUC), and the distance between the center of gravity to the Cartesian coordinate center are identified as parameters. We generated images based on these parameters, referred to as Pixel Shape Indices (PSI). PSI images were produced for a section of an OLI Landsat satellite image of the Los Angeles metropolitan area. Twenty-two datasets were created, combining original spectral bands with PSI images, and used for classification via the Maximum Likelihood classification scheme across four land use/land cover types. Classification Accuracy was assessed using the Kappa coefficient of agreement as well as the Hellden and Short coefficients. In most cases, results show improved values for all metrics, indicating enhanced classification performance across all land use / land cover categories due to the integration of PSI features. It is recommended that PSI images be incorporated into various image processing techniques- such as image fusion, filtering, enhancements, and texture analysis- for improved mapping and environmental monitoring.

Ensuring sustainable and profitable agriculture is critical for addressing global food security challenges. This has resulted in the need for automation in plant health identification. However, this objective is hampered by the lack of efficient image-processing methods and the need for extensive datasets for training deep learning models for plant disease diagnosis. To overcome the need for extensive training data, the proposed Localized Normalized Difference Vegetation Index (LNDVI) uses zero-shot plant detection models such as Grounded Dino and state-of-the-art methods for image segmentation such as Segment Anything Model (SAM) are leveraged. This also expands the capabilities of the system to diagnose plant health beyond known plant species available as part of training set. The proposed system uses synthetic Normalized Difference Vegetation Index (NDVI) to estimate the chlorophyll content of the plant through RGB images alone instead of using the combination of RGB and near Infra-red (nIR) bands used in contemporary works. Since NDVI value is greatly affected by the amount of light present while the image is captured, we also present an irradiation estimation metric that uses CIE XYZ (Tristimulus values), Hue, Saturation and Value (HSV) and CIE LAB color spaces as well as correlated color temperatures, which automatically normalizes the NDVI threshold for health classification of the image, enabling a more precise analysis of plant health. Using the Grounding Dino provided an accuracy of 99.994% in terms of detecting plants from the phenotyping dataset. The segmentation of plant region in images is reported using Intersection over Union (IoU). While using the Segment Anything Model (SAM), an average accuracy of 95.884% was obtained for clustered plants while the average accuracy was even better at 97.031% for individual plants. Significant differences were observed for plant health classification while using Localized Normalized Difference Vegetation Index (LNDVI) approach when compared to NDVI.

Spin-allowed Fe2+ absorption bands occur in the visible and near-infrared diffuse reflectance spectra of most pyroxenes. The wavelengths of the bands centered near 1 μm and 2 μm vary as functions of pyroxene composition, making possible mineralogical and chemical deductions based on spectral reflectance curves. Typically, pyroxene bands are well developed in relation to absorption features in the spectra of other rock-forming minerals and glass; thus pyroxene often dominates the spectral curves of pyroxene-bearing rocks. Telescopic spectra of the moon and some other solar system objects contain absorption features that can be interpreted in terms of pyroxenes mixed with other minerals and/or glass.

Hyperspectral technology is useful for urban studies due to its capability in examining detailed spectral characteristics of urban materials. This study aims to develop a spectral library of urban materials and demonstrate its application in remote sensing analysis of an urban environment. Field measurements were conducted by using ASD FieldSpec 3 Spectroradiometer with wavelength range from 350 to 2500 nm. The spectral reflectance curves of urban materials were interpreted and analyzed. A collection of 22 spectral data was compiled into a spectral library. The spectral library was put to practical use by utilizing the reference spectra for WorldView-2 satellite image classification which demonstrates the usability of such infrastructure to facilitate further progress of remote sensing applications in Malaysia.

When the typical hyperspectral image classification method is used for vegetation classification and extraction, its extraction accuracy and precision classification degree are difficult to meet the practical application requirements. Hyperspectral data acquisition is mainly accomplished by two kinds of sensors: non-imaging spectrometer and imaging spectrometer. This paper combines SCVHR 1024i portable hyperspectral instrument and ubert UHD185 UAV imaging spectrometer to collect spectral reflectance curve of soybean, through data processing to achieve the purpose of analysis of its growth, and has a strong guiding significance for large-scale monitoring of soybean nutrition, pests, pesticide residues and so on.

The image color recorded in a computer vision system depends on three factors: the physical content of the scene, the illumination on the scene, and the characteristics of the camera. The goal of computational color correction is to reset the intensities of the color channels in digital images, and express the scene colors canonically. In the color correction method based on spectral reflectance reproduction, the finite-dimensional linear-model is an effective way to condense spectral reflectance. Since there are only three channels for each pixel in digital images, color correction based on spectral reflectance reproduction using the finite-dimensional model with three dimensions is needed. In the definite gamut, the spectral reflectance curves are analyzed using the finite-dimensional model. It is shown that reflectance generated by three basis functions which is capturing 99.13% of the overall variance, provides good approximations to the measured spectral reflectance. Together with the measured spectral power distribution of the illumination and the estimated spectral responses of the camera, the quantum catches are calculated. Errors caused by approximation of the finite-dimensional model are relatively small, which shows that color correction based on the finite-dimensional linear-model has feasibility to some extent.

Analysis and classification of hyperspectral data for mapping land degradation: An application in southern Spain

Terrain classification is a challenging task for robots operating in unstructured environments. Existing classification methods make simplifying assumptions, such as a reduced number of classes, clearly segmentable roads, or good lighting conditions, and focus primarily on one sensor type. These assumptions do not translate well to off-road vehicles, which operate in varying terrain conditions. To provide mobile robots with the capability to identify the terrain being traversed and avoid undesirable surface types, we propose a multimodal sensor suite capable of classifying different terrains. We capture high resolution macro images of surface texture, spectral reflectance curves, and localization data from a 9 degrees of freedom (DOF) inertial measurement unit (IMU) on 11 different terrains at different times of day. Using this dataset, we train individual neural networks on each of the modalities, and then combine their outputs in a fusion network. The fused network achieved an accuracy of 99.98% percent on the test set, exceeding the results of the best individual network component by 0.98%. We conclude that a combination of visual, spectral, and IMU data provides meaningful improvement over state of the art in terrain classification approaches. The data created for this research is available at https://github.com/RIVeR-Lab/vast_data.

Two reflecting surfaces which are metameric under a particular illuminant must have spectral reflectance curves that intersect. The possibility of metamerism depends on the number and the spectral positions of the intersections and on the shape of the particular spectrum locus in the chromaticity diagram which applies to a given color space. The relevant rules are derived for ordinary trichromatic vision, and the extensions necessary when metamerism rests on more than three matching conditions (e.g., metamerism for both photopic and scotopic vision) are discussed.

The development of the Inter-Society Color Council-National Bureau of Standards (ISCC-NBS) system of color names, based on the standards in the Munsell Book of Color, made it necessary to specify the master standards of this book in fundamental terms. Accordingly, spectral reflection curves were run for each of the 421 master standards on the General Electric recording spectrophotometer at the National Bureau of Standards, using slit widths of approximately 4 millimicrons. Various corrections were applied to these spectrophotometric data in accordance with methods regularly used for such work at the bureau. Colorimetric computations were then made with these data, resulting in tristimulus specifications according to the 1931 ICI standard observer and coordinate system. Four illuminants were used: ICI Illuminants A and C, representative of incandescent-lamp light and average daylight, respectively, Illuminant D (lightly overcast north sky), and Illuminant S (extremely blue sky). The colorimetric specifications of the Munsell standards for all four illuminants are thus given. The trilinear coordinates for the Munsell standards calculated for ICI Illuminant C have been plotted on large chromaticity (x, y) diagrams and constant Munsell chroma lines drawn in. (Similar values obtained by Glenn and Killian at the Massachusetts Institute of Technology in 1935 for Munsell color standards bearing the same hue-value-chroma designations have also been plotted on the diagram and differences between the two sets of data are discussed.) These diagrams serve as means for determining the Munsell notation and thereby the ISCC-NBS color name for any color whose trilinear coordinates and apparent reflectance are given.

The sunglass industry has seen a major development in the past few years, though without the development of sunglass simulation tools for the wearers. This study describes a protocol to accurately simulate sunglass filtered vision using hyperspectral imaging and colour appearance model. The use of hyperspectral imaging ensures that the spectral transmittances of the sunglasses can be applied to the spectral radiances of the target scene. This filtered hyperspectral data can then be converted into a colour image using CIE XYZ tristimulus values and image colour appearance model (iCAM06). The study also identifies the protocol for the testing of these filtered images through psychophysical experiments and formulates the key parameters for testing. Furthermore, the study also categorizes the acquisition parameters for accurate and fast application of sunglasses on hyperspectral images. Objective testing on the optimized hyperspectral data revealed a reduction of data size by four times, without any loss of colorimetric accuracy. The study lays out a clear path for the future execution of psychophysical experiments to validate the accuracy of reproduction for these simulated images.

The Gaussian and Gaussian-sigmoidal primaries are introduced as initialization functions in the nonnegative matrix factorization (NNMF) method to achieve more consistent error statistics. The proposed NNMF method with multiplicative update algorithm is employed for reconstruction of spectral data of Munsell and Colorchecker DC datasets from the corresponding CIEXYZ tristimulus values and the resultant achievements are compared with those obtained from PCA and the classical NNMF techniques. Both colorimetric and spectrophotometric errors between the original and the reconstructed spectra are used for the evaluation of reconstruction methods. While run to run variability of the classical NNMF method with multiplicative update algorithm is significantly remarkable, the stability of the suggested technique is fairly high, similar to the PCA method. The ensuing findings show that the embedding of Gaussian initializing vectors in NNMF method provide the all positive bases that enhance both the accuracy of Gaussian method and the reliability of NNMF technique.

A linear interpolation method is applied for reconstruction of reflectance spectra of Munsell as well as ColorChecker SG color chips from the corresponding colorimetric values under a given set of viewing conditions. Hence, different types of lookup tables (LUTs) have been created to connect the colorimetric and spectrophotometeric data as the source and destination spaces in this approach. To optimize the algorithm, different color spaces and light sources have been used to build different types of LUTs. The effects of applied color datasets as well as employed color spaces are investigated. Results of recovery are evaluated by the mean and the maximum color difference values under other sets of standard light sources. The mean and the maximum values of root mean square (RMS) error between the reconstructed and the actual spectra are also calculated. Since the speed of reflectance reconstruction is a key point in the LUT algorithm, the processing time spent for interpolation of spectral data has also been measured for each model. Finally, the performance of the suggested interpolation technique is compared with that of the common principal component analysis method. According to the results, using the CIEXYZ tristimulus values as a source space shows priority over the CIELAB color space. Besides, the colorimetric position of a desired sample is a key point that indicates the success of the approach. In fact, because of the nature of the interpolation technique, the colorimetric position of the desired samples should be located inside the color gamut of available samples in the dataset. The resultant spectra that have been reconstructed by this technique show considerable improvement in terms of RMS error between the actual and the reconstructed reflectance spectra as well as CIELAB color differences under the other light source in comparison with those obtained from the standard PCA technique.