scholarly journals Classification of Multi-Frequency Polarimetric SAR Images Based on Multi-Linear Subspace Learning of Tensor Objects

2015 ◽  
Vol 7 (7) ◽  
pp. 9253-9268 ◽  
Author(s):  
Chun Liu ◽  
Junjun Yin ◽  
Jian Yang ◽  
Wei Gao
Keyword(s):  
Linear Subspace ◽  
Principal Component ◽  
Subspace Learning ◽  
Classification Performance ◽  
Polarimetric Sar ◽  
Third Order ◽  
Sar Images ◽  
Linear Discriminant ◽  
Multi Band

One key problem for the classification of multi-frequency polarimetric SAR images is to extract target features simultaneously in the aspects of frequency, polarization and spatial texture. This paper proposes a new classification method for multi-frequency polarimetric SAR data based on tensor representation and multi-linear subspace learning (MLS). Firstly, each cell of the SAR images is represented by a third-order tensor in the frequency, polarization and spatial domains, with each order of tensor corresponding to one domain. Then, two main MLS methods, i.e., multi-linear principal component analysis (MPCA) and multi-linear extension of linear discriminant analysis (MLDA), are used to learn the third-order tensors. MPCA is used to analyze the principal component of the tensors. MLDA is applied to improve the discrimination between different land covers. Finally, the lower dimension subtensor features extracted by the MPCA and MLDA algorithms are classified with a neural network (NN) classifier. The classification scheme is accessed using multi-band polarimetric SAR images (C-, L- and P-band) acquired by the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) over the Flevoland area. Experimental results demonstrate that the proposed method has good classification performance in comparison with the classic multi-band Wishart classifier. The overall classification accuracy is close to 99%, even when the number of training samples is small.

scholarly journals Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 487 ◽  
Author(s):  
Yeong-Hyeon Byeon ◽  
Jae-Neung Lee ◽  
Sung-Bum Pan ◽  
Keun-Chang Kwak
Keyword(s):  
Principal Component Analysis ◽  
Input Data ◽  
Principal Component ◽  
Subspace Learning ◽  
Individual Identification ◽  
Second Step ◽  
Third Order ◽  
Order Tensor ◽  
Linear Discriminant ◽  
The Third

In this study, we present a third-order tensor-based multilinear eigenECG (MEECG) and multilinear Fisher ECG (MFECG) for individual identification based on the information obtained by an electrocardiogram (ECG) sensor. MEECG and MFECG are based on multilinear principal component analysis (MPCA) and multilinear linear discriminant analysis (MLDA) in the field of multilinear subspace learning (MSL), respectively. MSL directly extracts features without the vectorization of input data, while MSL extracts features without vectorizing the input data while maintaining most of the correlations shown in the original structure. In contrast with unsupervised linear subspace learning (LSL) techniques such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), it is less susceptible to small-data problems because it learns more compact and potentially useful representations, and it can efficiently handle large tensors. Here, the third-order tensor is formed by reordering the one-dimensional ECG signal into a two-dimensional matrix, considering the time frame. The MSL consists of four steps. The first step is preprocessing, in which input samples are centered. The second step is initialization, in which eigen decomposition is performed and the most significant eigenvectors are selected. The third step is local optimization, in which input data is applied by eigenvectors from the second step, and new eigenvectors are calculated using the applied input data. The final step is projection, in which the resultant feature tensors after projection are obtained. The experiments are performed on two databases for performance evaluation. The Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, and Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The experimental results revealed that the tensor-based MEECG and MFECG showed good identification performance in comparison to PCA and LDA of LSL.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

Stacked auto-encoder for classification of polarimetric SAR images based on scattering energy

2019 ◽  
Vol 40 (13) ◽  
pp. 5094-5120
Author(s):  
Ronghua Shang ◽  
Yongkun Liu ◽  
Jiaming Wang ◽  
Licheng Jiao ◽  
Rustam Stolkin
Keyword(s):  
Polarimetric Sar ◽  
Sar Images

scholarly journals Characterization and classification of Romanian acacia honey based on its physicochemical parameters and chemometrics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mihaela Emanuela Crăciun ◽  
Oana Cristina Pârvulescu ◽  
Andreea Cristina Donise ◽  
Tănase Dobre ◽  
Dumitru Radu Stanciu
Keyword(s):  
Physicochemical Parameters ◽  
Stable Carbon Isotopes ◽  
Reducing Sugars ◽  
Activity Ratio ◽  
Sugar Content ◽  
Principal Component ◽  
Reducing Sugar ◽  
Linear Discriminant ◽  
Acacia Honey

AbstractThree groups of Romanian acacia honey, i.e., pure, directly adulterated (by mixing the pure honey with three sugar syrups), and indirectly adulterated (by feeding the bees with the same syrups), were characterized and discriminated based on their physicochemical parameters. Moisture, ash, 5-hydroxymethylfurfural (HMF), reducing sugars (fructose and glucose), and sucrose contents, free acidity, diastase activity, ratio between stable carbon isotopes of honey and its proteins (δ13CH and δ13CP) were evaluated. Adulteration led to a significant increase in sucrose content, HMF level, and Δδ13C = δ13CH‒δ13CP as well a decrease in reducing sugar content and diastase activity. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to experimental data in order to distinguish between pure and adulterated honey. The most relevant discriminative parameters were diastase activity, HMF, sucrose, and reducing sugar contents. Posterior classification probabilities and classification functions obtained by LDA revealed that 100% of honey samples were correctly assigned to their original group.

Texture modelling for land cover classification of fully polarimetric SAR images

2012 ◽  
Vol 3 (2) ◽  
pp. 129-148 ◽  
Author(s):  
Assia Kourgli ◽  
Mounira Ouarzeddine ◽  
Youcef Oukil ◽  
Aichouche Belhadj-Aissa
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Texture Modelling

Combining the Kernel Collaboration Representation and Deep Subspace Learning for Facial Expression Recognition

2018 ◽  
Vol 27 (08) ◽  
pp. 1850121 ◽  
Author(s):  
Zhe Sun ◽  
Zheng-Ping Hu ◽  
Raymond Chiong ◽  
Meng Wang ◽  
Wei He
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Subspace Learning ◽  
Superior Performance ◽  
Expression Recognition ◽  
Learning Networks ◽  
Linear Discriminant ◽  
High Level ◽  
Relationship Of

Recent research has demonstrated the effectiveness of deep subspace learning networks, including the principal component analysis network (PCANet) and linear discriminant analysis network (LDANet), since they can extract high-level features and better represent abstract semantics of given data. However, their representation does not consider the nonlinear relationship of data and limits the use of features with nonlinear metrics. In this paper, we propose a novel architecture combining the kernel collaboration representation with deep subspace learning based on the PCANet and LDANet for facial expression recognition. First, the PCANet and LDANet are employed to learn abstract features. These features are then mapped to the kernel space to effectively capture their nonlinear similarities. Finally, we develop a simple yet effective classification method with squared [Formula: see text]-regularization, which improves the recognition accuracy and reduces time complexity. Comprehensive experimental results based on the JAFFE, CK[Formula: see text], KDEF and CMU Multi-PIE datasets confirm that our proposed approach has superior performance not just in terms of accuracy, but it is also robust against block occlusion and varying parameter configurations.

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