Principal Component Transformation Research Articles

Sample data selection based on the feature subspace for photometric correction of the Chang'E-4 in situ observations

The lunar surface exhibits noticeable variations in photometric properties across different regions, influenced by their specific compositional and physical characteristics. These variations are particularly pronounced at the millimeter to centimeter scale. In this study, we propose a feature subspace-based sample selection method to identify Chang'E-4 Visible Near-infrared Imaging Spectrometer (CE-4 VNIS) in situ observations that share similar photometric properties at the centimeter scale. By constructing a feature subspace through principal component transformation, the CE-4 VNIS observation sites with comparable surface photometric properties are able to be selected from a series of observations. The selection results demonstrate that the majority of observation sites are covered by the lunar regolith, with a few exceptions consisting of rocks, soil bulk, shadows, and rough surfaces. These observations should be excluded from the photometric correction process during phase function fitting. Analyzing the photometric performance of the VNIS data with lunar regolith reveals that reflectance decreases as the phase angle increases below 80°, while above 80°, reflectance increases with increasing phase angle. This phenomenon is likely attributed to stronger forward scattering. Thus, a second-order polynomial is employed to fit the phase function for VNIS data. Consequently, a photometric model is developed for lunar regolith observed by CE-4 VNIS, incorporating the fitted phase function. Experimental results demonstrate reduced differences among observations with varying phase angles, and the corrected VNIS spectra exhibit a phase-reddening effect. These findings validate the effectiveness of the proposed model. A comparison between the corrected data with and without sample selection reveals a 33 % decrease in mean standard deviation for VIS/NIR bands and a 19 % decrease for SWIR bands in the corrected spectra after sample selection. These results indicate the potential applicability of our method for future VNIS observations, enabling the acquisition of photometrically consistent data.

Landscape ecological network construction method supported by high-precision GIS data

Abstract With the acceleration of urbanization, the regional landscape pattern has undergone profound changes. This study combines green space ecology and explores the construction method of landscape ecological networks using the support of GIS data platform. After completing the extraction of landscape environmental information, it is fused and processed using principal component transformation, tasseled cap transformation, and vegetation index extraction. Different landscape pattern indices are utilized to construct a landscape analysis pathway based on the relevant data from high-precision GIS. Meanwhile, an ecological sensitivity evaluation method that includes soil erosion and rocky desertification was proposed. By comparing and analyzing the landscape pattern indices, it was concluded that the Shannon diversity index increased by 0.2927 and the Shannon evenness index increased by 0.0352, indicating that the landscape types of the study area gradually tend to be even in quantity and spatial distribution. In the evaluation of ecological sensitivity, the proportion of moderate and lower sensitivity reaches 69.28%, indicating that the ecological environment is relatively calm despite external disturbances.

Study on Shadow Detection of World View-II Remote Sensing Image Based on Band Standardization

<p>This paper presents a valid shadow detection method subjected to WorldView-II remote images combining band standardization with principal component transformation algorithm. Eight bands among The WorldView-II image, each band indicates a specified range of values. Firstly, each band is standardized to the same scale, then utilizing three optimal bands forms a new single band. According to the characteristics of principal component, if the gray value of dark material is positive, just take it for granted, which will be used in next operation. Once the gray value is negative, it will be inverted comparing with the former. In this study, selecting the above adaption builds up an advanced shadow index. Experimental results show that the shadow index can distinguish the shadow from other objects effectively, such as water in high brightness, vegetation, roads, buildings. Meanwhile, there is no significant difference between the water in low brightness and high when using the shadow index. To cope with this problem, we propose an advanced water index based on the normalized differential water index, which is constructed to eliminate the interference of water in low brightness. Results indicate that the indexes can accurately distinguish shadow regions in the WorldView-II image data.</p> <p> </p>

Assessing the clinical utility of multi-omics data for predicting serous ovarian cancer prognosis

Ovarian cancer (OC) is characterised by heterogeneity that complicates the prediction of patient survival and treatment outcomes. Here, we conducted analyses to predict the prognosis of patients from the Genomic Data Commons database and validated the predictions by fivefold cross-validation and by using an independent dataset in the International Cancer Genome Consortium database. We analysed the somatic DNA mutation, mRNA expression, DNA methylation, and microRNA expression data of 1203 samples from 599 serous ovarian cancer (SOC) patients. We found that principal component transformation (PCT) improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than the decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. Our study provides perspective on building reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC. Impact statement What is already known on this subject? Recent studies have focussed on predicting cancer outcomes based on omics data. But the limitation is the performance of single-platform genomic analyses or the small numbers of genomic analyses. What do the results of this study add? We analysed multi-omics data, found that principal component transformation (PCT) significantly improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than did decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. What are the implications of these findings for clinical practice and/or further research? Our study provides perspective on how to build reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC for future studies.

Pixel-Level Assessment Model of Contamination Conditions of Composite Insulators Based on Hyperspectral Imaging Technology and a Semi-Supervised Ladder Network

Real-time and accurate supervision of contamination conditions of composite insulators can effectively prevent the occurrence of pollution flashover accidents and ensure stable operation of the power system. In this article, a refined and controllable artificial contamination experiment is carried out, in which a total of 60 types of pollutants (12 contamination conditions) are formed by combining 20 types of equivalent salt deposit densities (ESDDs) at four contamination levels and three types nonsoluble deposit densities (NSDDs)/ESDD ratios to pollute composite insulators. Hyperspectral imaging technology is also introduced to extract the spectral data of polluted insulators, and the changing pattern of spectral curves under different pollution conditions is studied. Then, the spectral curve is preprocessed by black-and-white correction, standard normal variate (SNV), and detrending correction, and the preprocessed data are analyzed for band correlation. According to the variation pattern of the spectral curve and the band correlation characteristics, the full set of bands is divided into red, other visible, and near-infrared regions for principal component transformation respectively. Compared with the traditional principal component transformation, the former can reduce the calculation amount by 66%. Finally, to solve the problem of insufficiently labeled samples in practical engineering, a semi-supervised ladder network is used to realize pixel-level evaluation of insulator contamination conditions, and evaluation results are visualized. Research results show the pixel-level evaluation method can accurately evaluate contamination conditions of different areas on the surface of composite insulators. A semi-supervised model can guarantee the accuracy of classification results when there are few labeled samples. When the supervision rate is 10%, the classification accuracy of a semi-supervised model for contamination grade, NSDD/ESDD ratio, and contamination condition is higher than 95%, while that of a supervised model is lower than 90%.

A Spectral–Spatial Method for Mapping Fire Severity Using Morphological Attribute Profiles

Fast and accurate fire severity mapping can provide an essential resource for fire management and studying fire-related ecological and climate change. Currently, mainstream fire severity mapping approaches are based only on pixel-wise spectral features. However, the landscape pattern of fire severity originates from variations in spatial dependence, which should be described by spatial features combined with spectral features. In this paper, we propose a morphological attribute profiles-based spectral–spatial approach, named Burn Attribute Profiles (BAP), to improve fire severity classification and mapping accuracy. Specifically, the BAP method uses principal component transformation and attributes with automatically determined thresholds to extract spatial features, which are integrated with spectral features to form spectral–spatial features for fire severity. We systematically tested and compared the BAP-based spectral–spatial features and spectral index features in the extremely randomized trees machine learning framework. Sentinel-2 imagery was used for seven fires in the Mediterranean region, while Landsat-8 imagery was used for another seven fires in the northwestern continental United States region. The results showed that, except for 2 fires (overall accuracy (OA) for EMSR213_P: 59.6%, EL: 59.5%), BAP performed well for the other 12 fires (OA for the 2 fires: 60–70%, 6 fires: 70–80%, 4 fires: >80%). Furthermore, compared with the spectral indices-based method, the BAP method showed OA improvement in all 14 fires (OA improvement in Mediterranean: 0.2–14.3%, US: 4.7–12.9%). Recall and Precision were also improved for each fire severity level in most fire events. Moreover, the BAP method improved the “salt-and-pepper” phenomenon in the homogeneous area, where the results are visually closest to the reference data. The above results suggest that the spectral–spatial method based on morphological attribute profiles can map fire severity more accurately.

An integrated approach for rapid exploration of carbonatites and related mineral resources

AbstractCarbonatites are proven significant repositories of several critical and strategic elements such as rare earth elements, niobium, thorium, and uranium. Owing to their economic significance, mapping of carbonatites and associated mineral deposits has occupied prominent place in mineral resource exploration programs. In this study an integrated approach was developed to map carbonatite and related mineral deposits in the Loe‐Shilman, Northwest Himalaya of Pakistan, using remotely sensed advance space‐borne thermal emission and reflection radiometer (ASTER) multispectral data and visible near infrared and short‐wave infrared (VNIR‐SWIR) spectral characteristics of minerals in these deposits. Several image enhancement techniques, including band ratio (i.e., B4/B3), principal component and minimum noise fraction transformation (PC6 and MNF5, respectively) helped in highlighting the targeted rocks. The results demonstrate the suitability of ASTER data for discriminating carbonatite related mineral deposits from other surrounding lithologies. Results obtained from these methods were validated through field observations in the area and further confirmed through petrographic and chemical analyses of collected specimens. Field data have also served as training data to perform a supervised classification, allowing further improvement of the mapping results. Moreover, the obtained results from the techniques used for exploring carbonatites and related mineral deposits were stacked together for comparison with each other, to check their sensitivities, and assess their efficiency and accuracy. Generally, all these methods successfully highlighted carbonatites and related mineral deposits; however, when used integratively they exhibit higher degree of accuracy, and has proven to be relatively rapid and cost‐effective.

Cross-track infrared sounder cloud fraction retrieval using a deep neural network

The retrieval of cloud fraction in satellite hyperspectral sounder field of view (FOV) is crucial for numerical weather prediction. This study proposes an innovative cloud fraction retrieval model for the hyperspectral infrared sounder - Cross-track Infrared Sounder (CrIS). The model is trained with a deep neural network (DNN), using the CrIS radiation spectra as the predictors and Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask as the learning target. An ensemble of randomly selected CrIS and VIIRS data are collocated and used as the training dataset. An optimized 5-layer neural network is built to establish the relationship between the CrIS spectra and the cloud fraction calculated from the VIIRS cloud mask within the CrIS FOV. In order to reduce the number of input predictors to enhance the efficiency of the model, a principal component transformation is performed on the original CrIS spectra and only the top 77 principal component scores are adopted as the final predictors. In general, the cloud fraction retrieved from the proposed DNN model are consistent with truth values calculated from the VIIRS cloud mask product. Further analysis on use cases demonstrates a slightly better cloud retrieval result during the daytime than that of the nighttime, and ocean retrievals are more accurate than land retrievals. However, since the relationship between CrIS spectrum and the cloud fraction is nonlinear, the model tends to slightly overestimate the cloud fractions over low cloud coverage regions and underestimate the values over high cloud fraction areas. Even so, the proposed model can still be a useful tool for obtaining cloud fraction information from hyperspectral infrared sounders and has the potential to be used for the numerical weather prediction and climate models, as well as other cloud studies.

Transfer-learning-based cloud detection for Zhuhai-1 satellite hyperspectral imagery

The Zhuhai-1 hyperspectral satellite can simultaneously obtain spectral information in 32 spectral bands and effectively obtain accurate information on land features through integrated hyperspectral observations of the atmosphere and land, while the presence of clouds can contaminate remote sensing images. To improve the utilization rate of hyperspectral images, this study investigates the cloud detection method for hyperspectral satellite data based on the transfer learning technique, which can obtain a model with high generalization capability with a small training sample size. In this study, for the acquired Level-1B products, the top-of-atmosphere reflectance data of each band are obtained by using the calibration coefficients and spectral response functions of the product packages. Meanwhile, to eliminate the data redundancy between hyperspectral bands, the data are downscaled using the principal component transformation method, and the top three principal components are extracted as the sample input data for model training. Then, the pretrained VGG16 and ResNet50 weight files are used as the backbone network of the encoder, and the model is updated and trained again using Orbita hyperspectral satellite (OHS) sample data to fine-tune the feature extraction parameters. Finally, the cloud detection model is obtained. To verify the accuracy of the method, the multi-view OHS images are visually interpreted, and the cloud pixels are sketched out as the baseline data. The experimental results show that the overall accuracy of the cloud detection model based on the Resnet50 backbone network can reach 91%, which can accurately distinguish clouds from clear sky and achieve high-accuracy cloud detection in hyperspectral remote sensing images.

A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine

Water resources are vital to the survival of living organisms and contribute substantially to the development of various sectors. Climatic diversity, topographic conditions, and uneven distribution of surface water flows have made reservoirs one of the primary water supply resources in Iran. This study used Landsat 5, 7, and 8 data in Google Earth Engine (GEE) for supervised monitoring of surface water dynamics in the reservoir of eight Iranian dams (Karkheh, Karun-1, Karun-3, Karun-4, Dez, UpperGotvand, Zayanderud, and Golpayegan). A novel automated method was proposed for providing training samples based on an iterative K-means refinement procedure. The proposed method used the Function of the Mask (Fmask) initial water map to generate final training samples. Then, Support Vector Machines (SVM) and Random Forest (RF) models were trained with the generated samples and used for water mapping. Results demonstrated the satisfactory performance of the trained RF model with the samples of the proposed refinement procedure (with overall accuracies of 95.13%) in comparison to the trained RF with direct samples of Fmask initial water map (with overall accuracies of 78.91%), indicating the proposed approach’s success in producing training samples. The performance of three feature sets was also evaluated. Tasseled-Cap (TC) achieved higher overall accuracies than Spectral Indices (SI) and Principal Component Transformation of Image Bands (PCA). However, simultaneous use of all features (TC, SI, and PCA) boosted classification overall accuracy. Moreover, long-term surface water changes showed a downward trend in five study sites. Comparing the latest year’s water surface area (2021) with the maximum long-term extent showed that all study sites experienced a significant reduction (16–62%). Analysis of climate factors’ impacts also revealed that precipitation (0.51 ≤ R2 ≤ 0.79) was more correlated than the temperature (0.22 ≤ R2 ≤ 0.39) with water surface area changes.

X-ray absorption spectroscopy study of Mn reference compounds for Mn speciation in terrestrial surface environments

Abstract X-ray absorption spectroscopy (XAS) offers great potential to identify and quantify Mn species in surface environments by means of linear combination fit (LCF), fingerprint, and shell-fit analyses of bulk Mn XAS spectra. However, these approaches are complicated by the lack of a comprehensive and accessible spectrum library. Additionally, molecular-level information on Mn coordination in some potentially important Mn species occurring in soils and sediments is missing. Therefore, we investigated a suite of 32 natural and synthetic Mn reference compounds, including Mn oxide, oxyhydroxide, carbonate, phosphate, and silicate minerals, as well as organic and adsorbed Mn species, by Mn K-edge X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopy. The ability of XAS to infer the average oxidation state (AOS) of Mn was assessed by comparing XANES-derived AOS with the AOS obtained from redox titrations. All reference compounds were studied for their local (<5 Å) Mn coordination environment using EXAFS shell-fit analysis. Statistical analyses were employed to clarify how well and to what extent individual Mn species (groups) can be distinguished by XAS based on spectral uniqueness. Our results show that LCF analysis of normalized XANES spectra can reliably quantify the Mn AOS within ~0.1 v.u. in the range +2 to +4. These spectra are diagnostic for most Mn species investigated, but unsuitable to identify and quantify members of the manganate and Mn(III)-oxyhydroxide groups. First-derivative XANES fingerprinting allows the unique identification of pyrolusite, ramsdellite, and potentially lithiophorite within the manganate group. However, XANES spectra of individual Mn compounds can vary significantly depending on chemical composition and/or crystallinity, which limits the accuracy of XANES-based speciation analyses. In contrast, EXAFS spectra provide a much better discriminatory power to identify and quantify Mn species. Principal component and cluster analyses of k2-weighted EXAFS spectra of Mn reference compounds implied that EXAFS LCF analysis of environmental samples can identify and quantify at least the following primary Mn species groups: (1) Phyllo- and tectomanganates with large tunnel sizes (2 × 2 and larger; hollandite sensu stricto, romanèchite, todorokite); (2) tectomanganates with small tunnel sizes (2 × 2 and smaller; cryptomelane, pyrolusite, ramsdellite); (3) Mn(III)-dominated species (nesosilicates, oxyhydroxides, organic compounds, spinels); (4) Mn(II) species (carbonate, phosphate, and phyllosilicate minerals, adsorbed and organic species); and (5) manganosite. All Mn compounds, except for members of the manganate group (excluding pyrolusite) and adsorbed Mn(II) species, exhibit unique EXAFS spectra that would allow their identification and quantification in mixtures. Therefore, our results highlight the potential of Mn K-edge EXAFS spectroscopy to assess bulk Mn speciation in soils and sediments. A complete XAS-based speciation analysis of bulk Mn in environmental samples should preferably include the determination of Mn valences following the “Combo” method of Manceau et al. (2012), EXAFS LCF analyses based on principal component and target transformation results, as well as EXAFS shell-fit analyses for the validation of LCF results. For this purpose, all 32 XAS reference spectra are provided in the Online Materials1 for further use by the scientific community.

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.

Spectral data processing based on maximum noise fraction

The processing of gamma spectrum measurement data has always been an important step in data interpretation of the survey area. It includes the correction of radioactivity interference caused by non-measuring targets, the correction of effects caused by different heights or topography in the survey environment, the smoothness and denoising of the measurement spectrum, etc. The accuracy of each step will affect the final data resources. The interpretation of the materials results in inaccurate regional radioactivity assessment. Maximum noise separation transformation is a similar method to principal component transformation, which is used to reduce dimensionality and noise of data. This paper takes the noise reduction process of gamma spectrum data processing as the research direction, uses SPSS data processing software, combines with MATLAB to denoise the data, and uses the maximum noise separation transformation as the denoising method, applies it to denoise the spectral data, and uses the principal component analysis method and the maximum noise fraction method to denoise the same set of data separately. After analysis, it is concluded that the maximum noise fraction method is superior to principal component analysis when there is a large noise variance for a set of data.

Machine Learning and Metabolomics: Diagnosis of Malignant Breast Cancer

Breast cancer is one of the leading causes of death among women in the US, and early diagnosis of the disease contributes significantly to the survival rates of patients. Metabolomics data, which can be obtained in a non-invasive manner through blood plasma sampling, coupled with machine learning methods, has been increasingly applied to the field of biology, opening up a novel dimension of quantitative analysis that has the potential to unlock the secrets to highly complex diseases such as cancer. In this study, machine learning methods were leveraged to analyze several hundred metabolomic signatures of breast cancer patients towards discrimination between malignant and benign tumors. Samples of plasma were taken from 100 female breast cancer patients (50 from malignant and 50 from benign tumors) and metabolite molecules such as lipids and bile acids were extracted using ultra-performance liquid chromatography coupled with tandem mass spectrometry. The data were then log-transformed, and metabolites with a significant log-fold-change were selected for dimensionality reduction using a Wilcoxon test. These data where then analyzed using an ensemble of 8 different models: decision tree, gradient boosting, random forest, elastic net, linear discriminant analysis, support vector machine, nearest shrunken centroids, and a neural network. The accuracy was evaluated using the ROC cuve and AUC. Variations on these models were included in analysis, including principal component transformations. The most successful model was built using recursive feature selection and a logistic regression model with L1 regularization, which had an accuracy score of 95.0% and a biomarker panel of 42 assembled metabolites from the original 358. With the Wilcoxon-selected feature set of 62 metabolites, gradient boosting and random forest algorithms also returned accuracy scores of up to 90% and with an AUC of 0.98. Future refinements on these models could eventually lead to the use of the biomarkers in a clinical setting, helping to improve survival rates through early detection of malignant breast cancer tumors.

A study on the use of UAV images to improve the separation accuracy of agricultural land areas

Classifying satellite images with medium spatial resolution such as Landsat, it is usually difficult to distinguish between plant species, and it is impossible to determine the area covered with weeds. In this study, a Landsat 8 image along with UAV images utilized to separate pistachio cultivars and separate weed from trees. To use the high spatial resolution of UAV images, image fusion was carried out through the high-pass filter, wavelet, principal component transformation, BROVEY, IHS, and Gram Schmidt methods. ERGAS, RMSE, and correlation criteria were applied to assess their accuracy. The results represented that the wavelet method with R2, RMSE, and ERGAS 0.91, 12.22 cm, and 2.05 respectively had the highest accuracy in combining these images. Then, images obtained by this method were chosen with a spatial resolution of 20 cm for classification. Different classification methods including unsupervised method, maximum likelihood, minimum distance, fuzzy artmap, perceptron, and tree methods were evaluated. Moreover, six soil classes, Ahmad Aghaei, Akbari, Kalleh Ghoochi, Fandoghi, and a mixing class of Kalleh Ghoochi and Fandoghi were applied, and also three classes of soil, pistachio tree and weeds were extracted from the trees. The results demonstrated that the fuzzy artmap method had the highest accuracy in separating weeds from trees, differentiating various pistachio cultivars with Landsat image and also classification with combined image and had 0.87, 0.79, and 0.87 kappa coefficients respectively. The comparison between pistachio cultivars through Landsat image and the combined image showed that the validation accuracy obtained from harvest has raised by 17% because of the combination of images. The results of this study indicated that the combination of UAV and Landsat 8 images affects well to separate pistachio cultivars and determine the area covered with weeds.

NDVI COMPUTATION OF LISS III IMAGES USING QGIS

Feature Selection and Extraction is a very significant and mandatory part in the domain of image processing. After the relevant preprocessing operations, the relevant features have to be extracted using suitable algorithms. In multispectral imagery, the features are identified and extracted based on the applications and objectives of the analysis such as color, texture, brightness, intensity etc. Some of the prominent algorithms used for feature extraction are mean shift algorithm, Principal Component transformation, Wavelet based Transformation, Local Binary Patterns etc. Texture based feature detection and extraction is the most prominent method adopted which involves multispectral images. With respect to hyperspectral images, dimensionality is a critical issue to be dealt appropriately.

PCA and projection pursuits on high dimensional data reduction

The flood of multimedia data has affected various fields of research, including multimedia retrieval, database management, data mining, machine learning, social media analysis, image processing and so on. Many digital multimedia data collections are made in various fields and application contexts not only in advertising or journalism, but also often used in the context of cultural heritage applications, web search, or geographical information. Digital multimedia data is created in various formats and media modalities and available worldwide every day. The higher the quantity of data, the higher the complexity, diversity, dimensions and multy modality, so that multimedia data grows exponentially. To reduce the impact of high-dimensional data, data reduction is carried out. Data reduction plays an important role in classification. Therefore the selection of data reduction techniques must be done carefully. Principal Component Transform is widely used in data reduction. This analysis attempts to eliminate correlations between bands and further determines the optimal linear combination of original data dimensions for variations in the value of data pixels. The mathematical principle of Principal Component depends on the decomposition of eigenvalues from the covariance matrix of the data matrix. When multimedia data is smaller or covered by data noise, Principal Component is no longer efficient at reducing. To overcome this, Projection Pursuit was introduced using sphering matrices and invariant translations in the transformations. In Projection Pursuits, only data that is considered interesting will be transformed. Optimization of reduced data selection is based on the maximum value of the projection index generated. The projection index used is Skewness and Kurtosis because it is considered in accordance with the data used, the CASI image of Bogor city.

Liver Lesions and Acute Intracerebral Hemorrhage Detection Using Multimodal Fusion

Medical image fusion is designed to help physicians in their decisions by providing them with a preclinical image with enough information. Accurate assessment and effective treatment of the disease reduce the time it takes to relieve the symptoms of the disease. This article utilizes an effective data fusion approach to work on two different imaging modalities; computed tomography (CT) and magnetic resonance imaging (MRI). The data fusion approach is based on the combination of singular value decomposition (SVD) and the Fast Discrete Curvelet Transform (FDCT) techniques to reduce processing time during the fusion process. The SVD-FDCT data fusion approach is being tested with two multimodal medical image fusion applications. The first application concerns the detection of liver lesions and the second application concerns the early detection of acute intracerebral hemorrhage. Experimental tests demonstrate that not only the SVD-FDCT data fusion algorithm can treat the curved objects and edges effectively as the FDCT do. But also, the throughput of the fusion algorithm is comparable to related fusion algorithms such as principal component analysis (PCA), Transform and Discrete Wavelet Transform (DWT), dual-tree complex wavelet transform (DT-CWT), and Curvelet fusion algorithms.

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.

Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.