Principal Component Analysis Transformation Research Articles

Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R2 of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies.

A Novel Approach Applied to Transient Short-Circuit Diagnosis in TIMs by Piezoelectric Sensors, PCA, and Wavelet Transform

Non-invasive fault diagnosis of three-phase induction motors (TIM) is widely used in industrial applications to ensure the integrity of processes. Among different types of TIM failures, transient inter-turn short circuits (ITSCs) are incipient stator winding faults characterized as short circuits between two or more turns of the coils, that can lead the winding to progressive deterioration, and consequently, the TIM to total failure. In this context, this article proposes a novel approach by using piezoelectric transducers, which performs the transient ITSC detection, phase identification, and magnitude classification by using the Acoustic Emission (AE) technique. To accomplish this analysis, a new statistical index based on the cross-correlation function was proposed to detect the ITSC and classify its magnitude. Besides, Wavelet Transform and Principal Component Analysis (PCA) stood out as promising tools to identify which phase was affected by the short circuits. A TIM was subjected to ITSCs and the experimental results showed that the proposed algorithm successfully performed the transient ITSC detection, phase identification, and evolution classification. Additionally, this work improve the capabilities of traditional AE systems, since no AE signal processing algorithm has ever been proposed for a comprehensive diagnosis of transient ITSC.

Panchromatic and Multispectral Image Fusion Combining GIHS, NSST, and PCA

Spatial and spectral information are essential sources of information in remote sensing applications, and the fusion of panchromatic and multispectral images effectively combines the advantages of both. Due to the existence of two main classes of fusion methods—component substitution (CS) and multi-resolution analysis (MRA), which have different advantages—mixed approaches are possible. This paper proposes a fusion algorithm that combines the advantages of generalized intensity–hue–saturation (GIHS) and non-subsampled shearlet transform (NSST) with principal component analysis (PCA) technology to extract more spatial information. Therefore, compared with the traditional algorithms, the algorithm in this paper uses PCA transformation to obtain spatial structure components from PAN and MS, which can effectively inject spatial information while maintaining spectral information with high fidelity. First, PCA is applied to each band of low-resolution multispectral (MS) images and panchromatic (PAN) images to obtain the first principal component and to calculate the intensity of MS. Then, the PAN image is fused with the first principal component using NSST, and the fused image is used to replace the original intensity component. Finally, a fused image is obtained using the GIHS algorithm. Using the urban, plants and water, farmland, and desert images from GeoEye-1, WorldView-4, GaoFen-7 (GF-7), and Gaofen Multi-Mode (GFDM) as experimental data, this fusion method was tested using the evaluation mode with references and the evaluation mode without references and was compared with five other classic fusion algorithms. The results showed that the algorithms in this paper had better fusion performances in both spectral preservation and spatial information incorporation.

Quantitative mapping of depth profile of fatigue cracks using eddy current pulsed thermography assisted by PCA and 2D wavelet transformation

Fatigue cracks are widely generated in numerous engineering components under alternating loads, whose sudden brittle fracture would seriously shorten the service life and cause significant hazards. A large number of nondestructive testing (NDT) techniques have been utilized to quantitatively evaluate the fatigue cracks, however, most of these methods are difficult to be used for a full characterization of the cracks’ depth profile. The eddy current pulsed thermography (ECPT) as a promising NDT technique shows great potential for fatigue crack evaluation due to its high detection efficiency, large inspection area, and high spatial resolution. In this article, a ferrite yoke was utilized to generate a homogeneous eddy current field, and as such to enhance the detection sensitivity and to provide a wide open-view area for the camera. The hybrid processing strategy combining principal component analysis (PCA) and 2D wavelet transformation was proposed to extract spatial features of the thermographic image sequences. Firstly, numerical results demonstrate that the profiles of slot cracks can be identified directly from the thermographic images in the early heating stage. Afterward, the ECPT experiments on slot cracks with different depth profiles demonstrate the feasibility of the hybrid processing strategy. Then, three fatigue cracks were manufactured through the three-point bending method and detected by ECPT. The results of the hybrid processing method indicate that these three fatigue cracks have elliptical profiles. Finally, the authentic depth profile of a fatigue crack with an elliptical shape is extracted through a destructive experiment. The comparison results between the real depth profile and the profile characterized through the proposed strategy show great consistency and validate the superiority of the proposed strategy.

Transfer learning for estimating occupancy and recognizing activities in smart buildings

Activities Recognition (AR) and Occupancy Estimation (OE) are vital to many smart systems that work on providing good services in smart buildings. Many applications, such as energy management need information like activities and occupancy to provide good assistance. Most of the previous research about AR and OE focused on applying supervised machine learning methods. Researchers train a model and evaluate it using data collected from the same environment (Domain). A model trained in a specific domain will not generalize well in other domains. Creating a trained model to every environment is not feasible due to the lack of data. Collecting sufficient data can be time consuming and infeasible in some cases. Computational power can be a challenge for researchers by increasing the training time due to the lack of the required computing resources. Using traditional machine learning methods, the obtained performance may be unsatisfactory, and can not lead to optimal solutions. For all these reasons, we need a solution that helps us overcome the stated problem and obtain models with acceptable results. In this work, we present and discuss different transfer learning methods that help us transfer knowledge from a source domain to a target domain. The goal is to reuse as much as possible information from the source domain to enhance the performance of the model at the target domain. This type of approaches will solve the problems mentioned before such as the lack of data and will provide us with good results due to the use of knowledge from multiple source domains. We tested five Transfer learning (TL) approaches: a principal component analysis (PCA)-like method that creates a transformation like the PCA transformation and apply it to the data to create new common domain, a PCA based method that creates common domain using PCA, a PCA-SMOTE method that balances the data and creates common domain, a basic method based on a simple matching between similar features from source and target domain, and a sparse coding-based method that creates a common domain where the data representation will be as sparse as possible. The impressive results that we obtained in both tasks prove that the presented methods can be applied to transfer knowledge across different domains.

Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas sensor with dynamic measurement and PCA weak separation

With the rampant drug crime, the detection of drug-producing chemicals has put forward the great demand of multi-type and multi-concentration on-line rapid detection. The rapid development of dynamic measurement for semiconductor gas sensors provides a solution to this problem. However, the mutual blend of type and concentration information negatively affects sensor performance. In this paper, principal component analysis (PCA) was used for weak separation of type and concentration; k-Nearest Neighbor (KNN) was used for qualitative recognition; polynomial regression was used for quantitative recognition. The physical meaning of the dynamic response signal after PCA transformation was first proposed: PC1 has a weak concentration meaning; the combination of PC2, PC3, and PC4 has a weak type meaning. Based on the weak separation, the stepwise recognition method of qualitative classification and quantitative regression was first used to improve the recognition rate, the resolution and the generalization performance of the sensor. Using the inverse transformation of PCA, the principle of PCA and the method of ideal data verified the feasibility of this method. The qualitative and quantitative recognition of various drug-producing chemicals had been realized, which is a new way of on-line rapid sensor detection for drug-producing chemicals.

Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning

This paper studies the feature extraction and middle-level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident. The image pyramid is constructed and used as the input of the CNN model, and the convolutional layer road traffic accident scene glass breakage and crack characteristics at each scale in the pyramid are extracted separately, and then the depth descriptors at different image scales are extracted. In order to improve the discriminative power of the depth descriptor, Hellinger kernel and Principal Component Analysis (PCA) are used to perform nonlinear and linear transformations. Two aggregation strategies based on depth descriptors are proposed to form a global image representation. The classification experiment of the data set shows that Hellinger kernel, PCA transformation, and two aggregation strategies are all conducive to improving the classification accuracy. In addition, the convolutional layer road traffic accident scene glass breaking and crack feature coding can obtain better classification performance than the fully connected layer feature. We conducted dynamic impact tests on plate glass and Polyvinyl Butyral- (PVB-) laminated glass under different boundary conditions and studied the crack propagation and failure process of the glass under different impact speeds. The results show that there are radial cracks and circular cracks on the glass specimens under the impact load; the glass specimens show partial damage under high-speed impact and the characteristics of glass breaking and cracks at the scene of road traffic accidents; the four-frame plate glass is supported by sharp dagger-like fragments. This paper compares the energy absorption of glass specimens under different boundary conditions. The results show that the energy absorption effect of the four-point supporting glass specimen is generally stronger than that of the four-frame supporting glass.

Dimensions reduction of vibration signal features using LDA and PCA for real time tool wear detection with single layer perceptron

This study uses the Linear Discriminant Analysis (LDA) method along with the Principal Component Analysis (PCA) method to reduce the dimensionality of the vibration signal feature classified by Single Layer Perceptron (SLP). The vibration features to be reduced are 10 out of 270 features selected based on the correlations analysis. The LDA and PCA transformations provide only three inputs, than the original 10 signal features for the SLP classifier. The Single Layer Perceptron is trained with a sequential incremental training approach using the perceptron learning rule. The training phase of SLP resulting Mean Squared Error (MSE) as low as 0.0840 and the validation phase in the CNC machining provides 97.5% accuracy with zero false alarms.

Prediction of Unknown Fault of Induction Motor using SVM following Decision-Directed Acyclic Graph

Prediction of unknown fault of induction motor is an important task to prevent it from unscheduled shutdown. Here, an unknown fault of induction motor has been classified and authenticated from other type of faults using multiclass support vector machine (SVM) following decision-directed acyclic graph (DDAG). Three phase current data samples are collected from induction motors with different known type of fault conditions and one induction motor with unknown type fault condition. Experiment has been performed for motor fault current signature analysis (MFCSA) to identify the type of unknown fault among the mixture of various type of faults. A feature extraction and dimensions reduction process called principal component analysis (PCA) is used to extract the information from fault current signature of each faulty motor and two eigenvalues of stator currents which are called principal components are effective fault features of the motors are captured with the help of PCA transformation. One vs one (OVO) SVM algorithm is applied to separate each pair of classes out of six classes nonlinearly by RBF type kernel assigning the unknown test sample to the class. The multiple PC values of each phase of each faulty induction motor are considered as one class. The OVO-SVM constructs n (n-1)/2 no of classifiers for n class problem of each phase and DDAG technique is used to create a directed acyclic graph using the classifiers to take accurate decision about the classification of the unknown fault. The unknown fault is classified for each phase among different type of faults depending on maximum membership count generated by classifiers and the fault is also authenticated from the results of DDAG of three phases.

A Machine Learning Method to Synthesize Channel State Information Data in Millimeter Wave Networks

In millimeter-wave (MMW) networks, the channel state information (CSI) carries essential information from the user to the base station (BS). The CSI values depend highly on the geometrical and physical features of the environment. Therefore, it is impossible to generate CSI data for computer simulations or analysis through mathematical models. The CSI in MMW networks can only be acquired through physical measurement(s) or with the help of expensive and complicated ray-tracing software. For many users, both these options are infeasible. This work aims to propose a simple and fast method that can generate artificial samples from the real data samples while ensuring that the artificial samples look similar to the real ones. The proposed method helps increase the size of existing CSI datasets and likely to benefit the evolution of deep learning models that need a large amount of training/testing data. The proposed method comprises two parts. (i) The first part applies data clustering and transformations such as principal component analysis (PCA)-based dimensionality reduction and probability integral transform (PIT) to convert the real data into a multivariate normal distribution of a smaller number of variables, and (ii) The second part synthesizes artificial data by learning from the multivariate normal distribution of the first part. The last step in the second part is to apply PIT and inverse PCA transformations to transform the artificial data into the same space as the input data. We compared the proposed method's performance with the well-known Kernel density estimation (KDE)-based methods that use Scott's rule and Silverman's rule to choose the bandwidth parameter value. The results show that the artificial samples generated by the proposed method exhibit very high similarity with the real ones as compared to the KDE-based methods.

ПОВЫШЕНИЕ РАЗРЕШАЮЩЕЙ СПОСОБНОСТИ СЛАБОКОНТРАСТНЫХ ИЗОБРАЖЕНИЙ В ПРОСТРАНСТВЕ ФАЗОВЫХ ХАРАКТЕРИСТИК СИНГУЛЯРНОГО РАЗЛОЖЕНИЯ

Рассмотрены информационные возможности фазовых характеристик, синтезируемых на основе метода сингулярного разложения применительно к решению задач сегментации при визуальном анализе слабоконтрастных изображений. Показано, что переход в новое пространство признаков, полученных на основе параметров ортогонального преобразования, обеспечивает повышение чувствительности и разрешаюшей способности при выявлении объектов интереса (аномальных областей). Приведены экспериментальные результаты на примере реальных изображениях.

A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study

Understanding the corrosion risk of a pipeline is vital for maintaining health, safety and the environment. This study implemented a data-driven machine learning approach that relied on Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), Feed-Forward Artificial Neural Network (FFANN), Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) to estimate the corrosion defect depth growth of aged pipelines. By modifying the hyperparameters of the FFANN algorithm with PSO and using PCA to transform the operating variables of the pipelines, different Machine Learning (ML) models were developed and tested for the X52 grade of pipeline. A comparative analysis of the computational accuracy of the corrosion defect growth was estimated for the PCA transformed and non-transformed parametric values of the training data to know the influence of the PCA transformation on the accuracy of the models. The result of the analysis showed that the ML modelling with PCA transformed data has an accuracy that is 3.52 to 5.32 times better than those carried out without PCA transformation. Again, the PCA transformed GBM model was found to have the best modeling accuracy amongst the tested algorithms; hence, it was used for computing the future corrosion defect depth growth of the pipelines. This helped to compute the corrosion risks using the failure probabilities at different lifecycle phases of the asset. The excerpts from the results of this study indicate that my technique is vital for the prognostic health monitoring of pipelines because it will provide information for maintenance and inspection planning.

Denoising of degraded face images sequence in PCA domain for recognition

In this paper, we propose an efficient algorithm for denoising the degraded face image sequence in the principal component analysis (PCA) domain for recognizing this face. We first apply a temporal filter that performs motion compensation combined with a weighted average filter, then an adaptative spatial filter realized by PCA transformation that decomposes the image temporally filtered into two sub-images using a threshold calculated according to the variance of noise and intensity retained by the first eigenvectors. The first sub-image containing a great intensity variance expresses the small features, and the other with high noise level and low intensity variance describes the large feature. Therefore, the spatial filter can be adapted according to the amount of information in each area, so that small features are reprojected in the Kernel PCA domain where image details are reconstructed efficiently, and large features are denoised by an anisotropic diffusion filter to recover the homogenous regions. Finally, the restored image is used in recognition process. The experimental results obtained from the Cohen-Kanade facial expression (CKFE) database tested against different noise levels and three types of blur (Gaussian, motion and pillbox) show better restoration and recognition performance of this algorithm compared to other methods.

Analysis of principal components used for modelling changes in glacitectonically disturbed areas

AbstractDetermination of vertical displacements of engineering objects is closely related to geodesic monitoring. Its purpose is to record the dynamics of changes in the deformation phenomenon. Geodesic monitoring requires the use of appropriate measurement equipment and appropriate methods for processing observation results, which make it possible to determine the correlation between the causes and effects of deformations in engineering objects. Progress in information technology resulted in the appearance of new methods for processing and compressing experimental data which are resistant to noise or interference and enable reduction of the amount of information.The paper presents a method for statistical analysis of multidimensional data based on PCA (principal component analysis) transformation, implemented with the use of a neural network. PCA transformation, related to the Karhunen–Loeve transformation, is used for processing signals regarded as stochastic processes. This method makes enables reduction of the input data space on the basis of independent principal components with due attention to their significance. It also makes it possible to model changes occurring in both buildings and terrain in glacitectonically disturbed areas.

The CCB-ID approach to tree species mapping with airborne imaging spectroscopy.

BackgroundBiogeographers assess how species distributions and abundances affect the structure, function, and composition of ecosystems. Yet we face a major challenge: it is difficult to precisely map species across landscapes. Novel Earth observations could overcome this challenge for vegetation mapping. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. In this paper, I describe a trait-based approach to species identification with imaging spectroscopy, the Center for Conservation Biology species identification (CCB-ID) method, which was developed as part of an ecological data science evaluation competition.MethodsThese methods were developed using airborne imaging spectroscopy data from the National Ecological Observatory Network (NEON). CCB-ID classified tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method inspired by botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis (PCA) and resampled to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale.ResultsCCB-ID received a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the competition test data. Accuracy and specificity scores were high for all species, but precision and recall scores varied for rare species. PCA transformation improved accuracy scores compared to models trained using reflectance data, but outlier removal and data resampling exacerbated class imbalance problems.DiscussionCCB-ID accurately classified tree species using NEON data, reporting the best scores among participants. However, it failed to overcome several species mapping challenges like precisely identifying rare species. Key takeaways include (1) selecting models using metrics beyond accuracy (e.g., recall) could improve rare species predictions, (2) within-genus trait variation may drive spectral separability, precluding efforts to distinguish between functionally convergent species, (3) outlier removal and data resampling can exacerbate class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) targeted feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support species mapping efforts.

Modified PCA Transformation with LWT for High-Resolution based Image Fusion

In this paper, we have adopted the combined approach to fuse images with spatial-domain and frequency-domain approach which has the advantages of both domains. The frequency-domain transformation is done with wavelet scheme, while modified principal component analysis (PCA) is used for spatial-domain transformation. Spatial-domain image fusion methods generally have poor performance because they produce spatial distortion in the fused image. The frequency domain methods have high computational complexity and provide great robustness. In this paper, we have proposed a method which is called modified LWT–PCA. The proposed method focuses on PCA transformation combined with frequency components obtained from LWT method to retain high resolution of image. Later, inverse PCA is performed to retirve the final image. Traditional discrete wavelet transform based on convolution requires massive computation and storage space. Wavelet transform based on lifting scheme can solve these computational complexity problems. Comparing to other multiscale transforms, wavelet transform provides better fused image. It has been observed that high correlation exists between the replaced components. The higher-resolution data ensures that the spectral information of the original image is maintained. We have obtained higher variance where gray level scattering is more, which is elaborated using experimental results. Other quantitative and qualitative results are presented in this paper which show that proposed method is better than other methods in literature.

Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

Hyperspectral (HS) pansharpening aims to generate a fused HS image with high spectral and spatial resolution through integrating an HS image with a panchromatic (PAN) image. A guided filter (GF) and principal component analysis (PCA) hybrid HS pansharpening method is proposed. First, the HS image is interpolated and the PCA transformation is performed on the interpolated HS image. The first principal component (PC1) channel concentrates on the spatial information of the HS image. Different from the traditional PCA method, the proposed method sharpens the PAN image and utilizes the GF to obtain the spatial information difference between the HS image and the enhanced PAN image. Then, in order to reduce spectral and spatial distortion, an appropriate tradeoff parameter is defined and the spatial information difference is injected into the PC1 channel through multiplying by this tradeoff parameter. Once the new PC1 channel is obtained, the fused image is finally generated by the inverse PCA transformation. Experiments performed on both synthetic and real datasets show that the proposed method outperforms other several state-of-the-art HS pansharpening methods in both subjective and objective evaluations.

Robust denoising technique for ultrasound images by splicing of low rank filter and principal component analysis

Robust image denoising techniques are still a significant challenge for medical ultrasound images. Though the difficulty of the recently proposed strategies, most techniques have not yet achieved desired level of applicability. The principle focus of this paper is a thresholding strategy together with a splicing of Low Rank Filtering (LRF) and Principal Component Analysis (PCA) techniques. By means of the proposed robust denoising technique, the denoised medical ultrasound image is assessed until the point that it reaches desired level. The input noisy medical ultrasound image is given to the hybridization technique of LRF mechanism and PCA transformation which is used for removing the noise in medical ultrasound image. For effective image denoising, the segmentation process is done with the guide of sparse decomposition framework. Consequently, denoised image threshold is compared with the threshold value of training images in the database and this is repeated until we get a better denoised image. For analysis purpose, the set of five medical ultrasound images of artery have been considered. These medical ultrasound images are used for quantitative analysis. Performance of these recent filters as two phase matrix decomposition denoised filter, new adaptive denoised filter, and fast fourier bessel steerable PCA (FFBsPCA) denoised filter have been compared with our proposed splicing denoised method in terms of peak signal to noise ratio (PSNR) value and mean square error (MSE) value performance index under various noise density selection. Analytical results for set of five medical ultrasound images have shown that this proposed splice filtering method is more robust for noise reduction. So, it has shown that proposed splice filtering method is robust for medical ultrasound image denoising which preserves the clinical details and minimizing the noise level in medical ultrasound images.

A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents

Vegetation variable retrieval from reflectance data is typically grouped into three categories: the statistical–empirical category, the physical category and the hybrid category (physical models applied to statistical models). Based on the similarities between the spectra of leaves in the optical domain, the leaf reflectance spectra can be linearly modelled using a very limited number of principal components (PCs) if the PCA (principal component analysis) transformation is carried out at the sample dimension. In this paper, we present a novel data-driven approach that uses the PCA transformation to reconstruct leaf reflectance spectra and also to retrieve leaf biochemical contents. First, the PCA transformation was carried out on a training dataset simulated by the PROSPECT-5 model. The results showed that the leaf reflectance spectra can be accurately reconstructed using only a few leading PCs, as the ten leading PCs contained 99.999% of the total information in the 3636 training samples. The spectral error between the simulated or measured reflectance and the reconstructed spectra was also investigated using the simulated and measured datasets (ANGERS and LOPEX’93). The mean root mean squared error (RMSE) values varied from 5.56 × 10−5 to 6.18 × 10−3, which is about 3–10 times more accurate than the PROSPECT simulation method for measured datasets. Secondly, the relationship between PCs and leaf biochemical components was investigated, and we found that the PCs are closely related to the leaf biochemical components and to the reflectance spectra. Only when the weighting coefficient of the most sensitive PC was employed to retrieve the leaf biochemical contents, the coefficients of determination for the PCA data-driven model were 0.69, 0.99, 0.94 and 0.68 for the specific leaf weight (SLW), equivalent water thickness (EWT), chlorophyll content (Cab) and carotenoid content (Car), respectively. Finally, statistical models for the retrieval of leaf biochemical contents were developed based on the weighting coefficients of the sensitive PCs, and the PCA data-driven models were validated and compared to the traditional VI-based and physically-based approaches for the retrieval of leaf properties. The results show that the PCA method shows similar or better performance in the estimation of leaf biochemical contents. Therefore, the PCA method provides a new and accurate data-driven method for reconstructing leaf reflectance spectra and also for retrieving leaf biochemical contents.

Information extraction and dynamic evaluation of soil salinization with a remote sensing method in a typical county on the Huang-Huai-Hai Plain of China

Monitoring the dynamics of soil salinization is of great importance for agricultural production. This study selected Yucheng County, a typical county on the Huang-Huai-Hai Plain (HHHP) of China, as the study area and evaluated the spatial and temporal variation of soil salinization. Three methods, consisting of principal component analysis (PCA) transformation, tasseled cap (TC) transformation, and optimal band combination (OBC), were used to extract information from an early Landsat multispectral scanner (MSS) image from 1984, and their advantages were compared. In addition, OBC was used on a thematic mapper (TM) image from 2009. An iteratively self-organizing data analysis algorithm was used together with prior knowledge of likely classifications to interpret the MSS and TM images for data classification. Finally, a transfer matrix method was used to assess the spatial and temporal variability of soil salinization and analyze the driving factors of soil salinization. Compared to PCA transformation and OBC, TC transformation was a more effective method for extracting soil salinization information from the MSS sensor. The results indicate that a soil area of approximately 298 km2 was affected by salinity in 1984 in Yucheng County, of which 5.40%, 11.96%, and 12.75% were classified as being subject to slight, moderate, and severe salinization, respectively. In 2009, the saline area was reduced to only 146 km2, of which 10.70% and 3.75% were characterized by slight to moderate salinization and no severe salinization, respectively. The saline land decreased at an average rate of 6 km2 per year. This decrease was probably a result of lower groundwater depth, increased organic fertilizer or crop straw in soil, changed land use type, and increased vegetation coverage.