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- 摘要
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- 实验方案
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A novel iterative PCA–based pansharpening method
摘要： Image pansharpening methods are usually grouped into two main classes: the spectral methods and the spatial methods. For the first class, the multispectral image undergoes a spectral transformation and then one of the resultant components is totally substituted with the panchromatic image, hence leading to a considerable color distortion compared with the second class. In the literature, this issue is addressed by integrating the wavelet transform to the spectral methods in order to transfer only the spatial details of the panchromatic image. Furthermore, the spatial information quantity transferred during the fusion is usually defined by the resolution ratio between the multi-spectral and panchromatic images, and this is, however, not necessarily the optimal quantity providing the best images. Therefore, a simple iterative Principal Component Analysis (PCA) based method is proposed in this letter, to continuously transfer the spatial information from the panchromatic to the multispectral image until the best fused image is obtained. The spatial distortion Ds of the Quality with No Reference (QNR) index is used as a stopping criterion. The experiments applied on the Worldview–3 images show that the suggested method presents the best visual and numerical results comparatively to the PCA and the Additive Wavelet Principal Component (AWPC) methods.
关键词： wavelet transform,QNR index,pansharpening,spatial information,PCA
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - ROBUST PCANet for Hyperspectral Image Change Detection
摘要： Deep learning is an effective tool for handling high-dimensional data and modeling nonlinearity, which can tackle the hyper-spectral data well. Usually deep learning methods need a large number of training samples. However, there is no labeled data for training in change detection (CD). Considering these, this paper develops an unsupervised Robust PCA network (RPCANet) for hyperspectral image CD task. The main contributions of this work are twofold: 1) An unsupervised convolutional neural networks named RPCANet is proposed to handle the hyperspectral image CD; 2) An effective CD framework using the RPCANet and change vector analysis (CVA) is designed to achieve better CD performance with more powerful features. Experimental results on real hyperspectral datasets demonstrate the effectiveness of the proposed method.
关键词： change detection (CD),Robust PCA network (RPCANet),Hyperspectral image,change vector analysis (CVA)
更新于2025-09-09 09:28:46
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Stability Indicating 1st Derivative Synchronous Spectrofluorimetric Method for the Determination of the Newly Approved Antiviral Drug Daclatasvir in Presence of Its Oxidative and Photolytic Degradation Products: Application to Tablet Dosage Form
摘要： A highly sensitive, simple and rapid first derivative synchronous spectrofluorimetric method was utilized for the determination of daclatasvir dihydrochloride (DCV) in presence of its oxidative and photolytic degradation products. Where synchronous 1st derivative spectrofluorimetric approach was utilized to quantitatively determine DCV at 373 nm in presence of its oxidative degradation product and at 388 nm in presence of its photolytic degradation product that is obtained by exposing DCV to UV light at 312 nm, these were the zero-crossing wavelengths of degradation products without interference. The synchronous fluorescence was scanned at Δ λ of 80 nm. The method was found to be linear across the concentration range of 0.5-5.0 ng/mL with lower detection limit of 0.090 and lower quantification limit of 0.275 ng/mL (at 373 nm) and 0.268 ng/mL (at 388 nm). The adopted approach was successfully applied to commercial tablet and the results exhibited that the derivative synchronous fluorescence spectroscopy is a stability- indicating method, suitable for routine use within a short analysis time. The proposed method was carefully validated for linearity, accuracy, precision, specificity and robustness.
关键词： UPLC,Rhodiola rosea,Chemometrics,Glycyrrhiza uralensis,PCA,NO scavenging,Angelica sinensis
更新于2025-09-09 09:28:46
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0535 NIR technology as a process analytical tool for cheese inspection
摘要： The chemical composition of cheese is significantly related to quality, as it is responsible for its shelf life, yield and texture. However, conventional methods for determination of chemical composition are laborious and time-consuming. Fast assessment tools such as NIR spectroscopy and spectral imaging could be used for the quality control of cheeses. The advantage of this technique is that it allows several constituents to be measured simultaneously in a quick and nondestructive way. The objective of this work was to investigate spectral imaging combined with principal component analysis (PCA) for assessment of cheese samples. Spectral information of six varieties of cheese (Cheddar, coalho, Minas, mozzarella, prato and block processed cheese) were obtained using a spectral imaging system, between 928 and 2524 nm, with 6 nm intervals, resulting in 256 analyzed wavelengths. The first two principal components were responsible for 98.2% of the variation among samples, and the score plot presented good separation among samples of coalho cheese, mozzarella and Minas cheese. Loadings show that some peaks are strongly influenced by wavebands, associated to chemical bonds related to protein and fat. Spectral imaging combined with multivariate analysis can be a potential tool for fast cheese quality assessment.
关键词： quality control,spectral imaging,PCA
更新于2025-09-09 09:28:46
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Multiplex body fluid identification using surface plasmon resonance imaging with principal component analysis
摘要： Body ?uid identi?cation is a key component in forensic casework, providing important information for the reconstruction of criminal events. Body ?uid identi?cation in combination with DNA analysis allows the linking of individuals to criminal acts and can therefore be of great importance in determining the outcome of criminal court cases. However, none of the current body ?uid identi?cation methods meet all forensic requirements, such as a high sensitivity, a high speci?city and the ability to analyse multiple body ?uids in a single run. In this pilot-study, we explore, for the ?rst time, surface plasmon resonance imaging (SPRi) with antibody-based detection to serve as a novel multiplex body ?uid identi?cation assay for blood, semen, saliva, urine and sweat using minimal sample preparation. A training set consisting of ten donors per body ?uid was analysed to determine whether body ?uid speci?c response signals could be obtained. Principal component analysis (PCA) was performed as a statistical tool to cluster the body ?uid samples by response signal pattern reduction and to uncover the sources of variation between the body ?uids. Four principal components allowed complete clustering of all body ?uid types. Blind testing of body ?uid samples revealed that ?ve out of eight unknown samples could correctly be clustered to their corresponding group, three out of eight samples were identi?ed as inconclusive. Although optimization of the current SPRi method is required for use in the forensic ?eld, this pilot-study demonstrates the feasibility of SPRi to di?erentiate ?ve forensically relevant body ?uids.
关键词： Body ?uid identi?cation,Antibody,Forensic science,Surface plasmon resonance (SPR),Principal component analysis (PCA)
更新于2025-09-04 15:30:14
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Circular trace transform and its PCA-based fusion features for image representation
摘要： To improve the image representation efficiency of trace transform (TT) features to images with circular and arc-shaped textures, the authors propose circular TT (CTT) to extract features. CTT consists of tracing an image with circles around which certain functionals of the image function are calculated. Quadruple CTT features can be generated through three successive functionals in the results of CTT, while different quadruple features can be obtained by choosing different combinations of successive functionals. These quadruple features can represent different texture properties and deeper intrinsic information of an image. By fusing CTT features and TT features based on PCA (FFCT_PCA), they construct a new complementary descriptor with much less dimension, further improving the representation performance for mixed texture images. Experimental results demonstrate that CTT has better performance than TT in recognising images with circular and arc-shaped textures, and FFCT_PCA has the potential to outperform the state-of-the-art feature extraction methods.
关键词： texture classification,image representation,circular trace transform,PCA-based fusion features
更新于2025-09-04 15:30:14
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Hyperspectral Anomaly Detection Using Collaborative Representation With Outlier Removal
摘要： Recently, collaborative representation detector (CRD) has been popularly used for hyperspectral anomaly detection. For the original CRD, the least squares solution becomes more unstable when more classes, i.e., samples for anomaly detection are involved, and the detection error is likely to happen if the test pixel is an anomalous pixel and several samples from background are similar anomalous. In this paper, we propose a hyperspectral anomaly detection method that uses CRD with principal component analysis (PCA) for removing outlier (PCAroCRD). According to the different background modeling methods, global and local versions are proposed. In the proposed algorithm, the spatial-domain PCA is adopted to extract main pixel information of global/local background that will be used as samples for collaborative representation, and simultaneously the information of abnormal pixels in global/local background can be removed. Fewer useful samples can also keep the detection result stable. Experimental results indicate that the PCAroCRD outperforms the original CRD, kernel version of CRD, advanced CRD (CRDBORAD), morphology-based CRD, Global Reed–Xiaoli (RX) algorithm, and the Local RX.
关键词： hyperspectral imagery,target detection,collaborative representation (CR),PCA,Anomaly detection
更新于2025-09-04 15:30:14
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Fusion of United Sparse Principal Component Analysis Dictionary Based on Linear Unmixing Image Technique
摘要： Based on the linear unmixing images of different surface objects, online dictionary learning algorithm was utilized to compute the sparse dictionaries for multispectral linear unmixing images and panchromatic images. Principal component analysis (PCA) was then utilized to generate united sparse PCA dictionaries through the extraction of the first principal components of panchromatic images and unmixing image dictionaries. The number of dictionaries is determined to be 480 after taking into consideration of the limitation in computing power and root-mean-square error of restructured images. Based on these dictionaries, orthogonal matching pursuit method was utilized to calculate the sparse coefficients of multispectral and panchromatic images, separately, while nonnegative matrix factorization fusion algorithm was utilized to calculate multispectral and panchromatic sparse coefficients to obtain sparse coefficient of the fusional image on all bands, with the resulted matrix having a size of 480 × 255 025. These united sparse PCA dictionaries and fusion sparse coefficients were then used to reconstruct the fusional image. Through the analysis of five quantitative indices of fusion assessment, the proposed fusion algorithm has retained the multispectral information of images and enhanced the detailed information in image texture.
关键词： nonnegative matrix factorization (NMF) fusion,principal component analysis (PCA) dictionary,Linear unmixing,orthogonal matching pursuit (OMP) algorithm,online dictionary learning (ODL) algorithm
更新于2025-09-04 15:30:14
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[IEEE 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Bangalore (2018.2.9-2018.2.10)] 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Determination of Absolute Heart Beat from Photoplethysmographic Signals in the Presence of Motion Artifacts
摘要： In Wireless Body Area Networks (WBANs), accurate monitoring of heart rate (HR) using Photoplethysmography (PPG) signals is always a difficult task, especially when the subjects are under radical exercises. This is due to the signals corrupted by severely strong Motion Artifacts (MA) caused by the subject’s body movements. In this work, a novel approach has been proposed consisting of signal decomposition for denoising using principal component analysis (PCA), spare signal reconstruction (SSR), peak detection and tracking and support vector machine (SVM) classifier for accurate estimation of HR, based on the wrist type PPG signals. With this approach, we are able to achieve high accuracy and also, it is strong enough to remove MA. Experiments were conducted on 12 subjects and their datasets are obtained from 2015 IEEE Signal Processing CUP, running on a threadmill with varying speeds ranging from 0 to a maximum speed of 15 km/hour. From the results, it is observed that the average absolute error of heart rate estimation is 1.66 beats per minute (BPM).
关键词： SVM classifier,PCA,HR,Wireless Body Area Networks (BAN),SSR,Accelerometer,PPG
更新于2025-09-04 15:30:14
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Testing a Modified PCA-Based Sharpening Approach for Image Fusion
摘要： Image data sharpening is a challenging field of remote sensing science, which has become more relevant as high spatial-resolution satellites and superspectral sensors have emerged. Although the spectral property is crucial for mineral mapping, spatial resolution is also important as it allows targeted minerals/rocks to be identified/interpreted in a spatial context. Therefore, improving the spatial context, while keeping the spectral property provided by the superspectral sensor, would bring great benefits for geological/mineralogical mapping especially in arid environments. In this paper, a new concept was tested using superspectral data (ASTER) and high spatial-resolution panchromatic data (WorldView-2) for image fusion. A modified Principal Component Analysis (PCA)-based sharpening method, which implements a histogram matching workflow that takes into account the real distribution of values, was employed to test whether the substitution of Principal Components (PC1–PC4) can bring a fused image which is spectrally more accurate. The new approach was compared to those most widely used—PCA sharpening and Gram–Schmidt sharpening (GS), both available in ENVI software (Version 5.2 and lower) as well as to the standard approach—sharpening Landsat 8 multispectral bands (MUL) using its own panchromatic (PAN) band. The visual assessment and the spectral quality indicators proved that the spectral performance of the proposed sharpening approach employing PC1 and PC2 improve the performance of the PCA algorithm, moreover, comparable or better results are achieved compared to the GS method. It was shown that, when using the PC1, the visible-near infrared (VNIR) part of the spectrum was preserved better, however, if the PC2 was used, the short-wave infrared (SWIR) part was preserved better. Furthermore, this approach improved the output spectral quality when fusing image data from different sensors (e.g., ASTER and WorldView-2) while keeping the proper albedo scaling when substituting the second PC.
关键词： Image fusion,ASTER,empirical line,PCA,WorldView-2,sharpening,Landsat 8,histogram matching
更新于2025-09-04 15:30:14