Top PDF About Classification Methods Based on Tensor Modelling for Hyperspectral Images

About Classification Methods Based on Tensor Modelling for Hyperspectral Images

About Classification Methods Based on Tensor Modelling for Hyperspectral Images

than a global procedure by sequentially searching the projection. But all these matrix algebra methods require a preliminary step which consists in vectorizing the images. Therefore, they rely on spectral properties only, neglecting the spatial rearrangement. To overcome these disadvantages, [9-11] recently introduced a new HSI representation based on tensor. This representation involves a powerful mathematical framework for analyzing jointly the spatial and spectral structure of data. For this investigation, several tensor decompositions have been introduced [12-14]. In particular, the Tucker3 tensor decomposition [15,16] yields the generalization of the PCA to multi-way data. An extension of this decomposition can generalize the lower rank matrix approximation to tensors. This multilinear algebra tool is known as lower rank-(K1, K2, K3) tensor approximation [17], denoted by LRTA-(K1, K2, K3). In [1] multi-way filtering algorithms are developed and the shown results demonstrate that this new way to built the Wiener filter is very efficient for the multidimensional data such as HSI.
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PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED  SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY

PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY

Extended morphological profile (EMP) is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs) at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectral- spatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA) C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.
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UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

Principal component analysis (PCA) (Fukunaga, 1990) and locality preserving projection (LPP) (He and Niyogi, 2004) are Commonly used unsupervised feature extraction methods. They use all pixels in hyperspectral image without knowing their labels. PCA maintains general information of data by maximizing covariance matrix. LPP preserves local structure of data by forming adjacency graph. semisupervised feature extraction algorithms use unlabeled samples beside labeled samples. Among which are semisupervised Marginal Fisher Analysis (SSMFA) (Huang et al., 2012), semisupervised local discriminant analysis (SELD) (Liao et al., 2013) and semisupervised feature extraction based on supervised and fuzzy-based linear discriminant analysis (SLDA) (Li et al., 2015). These methods maintain general structure of data and increase class separability by using unlabeled and labeled samples.
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USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION

USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION

By remote sensing we can view the earth, through windows of the electromagnetic spectrum that would not be humanly possible. Remote sensing includes both data acquisition and data interpretation. The image analysis part can be added to the data acquisition system to model the entire RS system. The analysis part is a key component of RS system, because we can change the data to information via it. The most important technique to extract information, from remotely sensed images, is classification. .The limitation of traditional classification methods is that each pixel is assigned to in a single class by presuming all pixels within the image are pure. The aim of this research can be explained by looking at the problems of mixed pixels in classification. These aims are: processing of mixed pixels, the primary aim of this research is evolution and processing of mixed pixels to improvement accuracy of the classification results and classification of mixed pixels, the seconding aims of this research is to provide knowledge about the decomposition of mixed pixels (spectral unmixing) on the hyperspectral image. New image processing tools and techniques, such as spectral unmixing, can be investigated through introducing new sensors with hyperspectral capabilities. Then, next section introduces the preparation of data for classification methods. Section 3 discusses the experimental data and evaluation and compares works that are performed on the hyperspectral images. Section 4, concludes the research.
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A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN  HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE  SPECTRAL COMPARISON

A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE SPECTRAL COMPARISON

Although the abundant bands play an important role in the TD, the high correlation among bands makes hyperspectral data greatly redundant, leading to a problem such as 'Hughes phenomena [Hughes, 1968]. Therefore, band selection (BS), which can be used to address this problem, has received increasing attention. However, most of the existing BS methods for hyperspectral data are classification orientated and few of them are proposed specially for TD. This study presents a novel but simple TD method aimed at quickly detecting the good bands in hyperspectral images.
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Analysis of vegetation dynamics using time-series vegetation index data from Earth Observation Satellites

Analysis of vegetation dynamics using time-series vegetation index data from Earth Observation Satellites

NDVI S10 images from SPOT VGT from 1999 to 2011 were processed for Rondonia. As precipitation has consider- able influence on the vegetation development, the most relevant period for this region is from April to September. Furthermore, the presence of clouds limits the use of satellite data in the rainy season. Maximum Value Compo- site images of January and AprilSeptember using NDVI VGT data from 2000 are presented in Figure 4. Due to the frequent cloud cover, the NDVI values from January might not be clearly related to the vegetation type and condition. In the remaining images, it was noticeable that the central areas of Rondonia have lower NDVI values. This is because this part of the state has low height herbaceous or semi- herbaceous vegetation, as the soil is mostly used for agriculture and pasture. Moreover, Rondonia is a remark- able example of land cover change in the past decades as a result of deforestation induced by human and natural causes. The converted forest cover in a fishbone pattern, mainly due to forestation caused by agricultural and urban expansion as reported by Eva et al. (2002), is noticeable in Figure 4. The least influence of clouds occurred between June and August. During this period a large increase in NDVI occurred, especially in forests. The better transpar- ency of the atmosphere at this time of the year promotes a net balance of radiation greater than the other parts of the year (Da Rocha et al., 2004; Malhi et al., 2002). This high availability of energy and the ability of forestry trees to capture water from deep soil explain the trend of the NDVI increase in forest areas between June and August.
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Classifiers ensemble in remote sensing: a

Classifiers ensemble in remote sensing: a

The quality of LCLU map produced from the classification of a satellite image is estimated by measuring the accuracy between the classification of the land-cover made by any method and the reality. It is obvious that it is impossible to check the behaviour of every method at any surface unit (pixel), hence in our case; we do the validation using the testing sample. The most common way to do this comparison is by using a confusion/error matrix (Foody, 2002). In this method, the classification obtained by the methods at one place is compared with the class defined in the testing observation. If both classify in the same way, we could say that there is concordance, if not there is discordance. At the end, we sum up all the concordances divided by the number of testing observations and we have the Global Accuracy of the method.
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A review of the quantification and classification of pigmented skin lesions: from dedicated to hand-held devices

A review of the quantification and classification of pigmented skin lesions: from dedicated to hand-held devices

desktop computers. Ramlakhan & Shang [15] developed a mobile system which extracts color and shape features (e.g., hull/contour ratio, circularity index), and employs a k-NN classifier for diagnosis. The results show a 66.7% accuracy, 60.7% sensitivity and 80.5% specificity for malignancy, for an average processing time of 2-5 seconds. Doukas et al. [17] present a mobile application that performs part of the image processing locally (segmentation, feature extraction, classification), but may also use a cloud service to perform the classification. After testing several classification schemes, it was concluded that SVM performed best, with a 77.06% accuracy level for benign lesions, and several classifiers (e.g., SVM) performed to an accuracy level of 85-90% for melanoma diagnosis. SKINcure © [109] relies on a remote server (located at the University of Bridgeport, D-Best Lab, USA) to perform image analysis. The application algorithm extracts five different features sets – 2D Fast Fourier Transform, 2D Discrete Cosine Transform, Complexity Feature Set, color Feature Set, Pigment Network Feature Set –, plus an additional four features – Lesion Shape Feature, Lesion Orientation Feature, Lesion Margin Feature and Lesion Intensity Pattern Feature. The features are then fed into a two-level classifier system for diagnosis (Table 8).
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EXTRACTING TEMPORAL AND SPATIAL DISTRIBUTIONS INFORMATION ABOUT MARINE MUCILAGE PHENOMENON BASED ON MODIS SATELLITE IMAGES

EXTRACTING TEMPORAL AND SPATIAL DISTRIBUTIONS INFORMATION ABOUT MARINE MUCILAGE PHENOMENON BASED ON MODIS SATELLITE IMAGES

accumulation of such water also affects, in addition to the availability of nutrients vehicled, the stability of the water column due to density gradients that are created between the hot water and less salted surface waters and dense background, saltier and colder. Some studies demonstrate how some season’s rich mucilaginous phenomena have been characterized by a prior increase in stability of the water column (Degobbis et al., 2000). Finally, as regards temperature, spectral analysis of time series of temperature anomaly spring and the presence / absence of mucilage on meteorological data for northern Italy, in general, and some stations Adriatic, in particular, seems to indicate a correlation between the positive anomalies and events of mucilage, which occur common with frequencies of about 6 and 3 years. However, in evaluating these results must take into account the relative limitation of extension of the time series and the uncertainty in the definition of the events of mucilage. This definition includes all the events reported and should be considered that the grouping into clusters is less clear if we take into account only the events considered to be certain documented (Giani M, 2002). Trend anomaly of mean temperature of the air spring to the Po Valley area used for comparison with the episodes of mucilage occurred during the summer. Also shows the moving average of the weather variable calculated on 10 years. (Deserti M. et al, 2003).
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Concealing the Level-3 features of Fingerprint in a Facial Image

Concealing the Level-3 features of Fingerprint in a Facial Image

For Example: In any application the facial image is presented on the top of the card and fingerprint image of a person will be stored hidden state in a smart card that he/she carries . At an access control site, the facial image of the user will be sensed and it will be compared to the fingerprint stored(hidden)on his/her smart card. Along with this facial matching, our proposed system will extract the fingerprint information hidden in the facial image. The recovered fingerprint will be used as a second source of authenticity either automatically or by a human in a supervised biometric application. The block diagram of the proposed system is given in the fig 1.
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COMPANY VALUATION METHODS BASED ON PATRIMONY

COMPANY VALUATION METHODS BASED ON PATRIMONY

A performance analysis should not be solely based on these methods for these 2 types of balance sheets show a static analysis at one moment in time. The companies can apply some “make-up” techniques to the balance sheet data. Therefore, a company that wants to show that it has high liquidity, can get a long- term loan, which will appear in the upper portion of the balance sheet and which will be repaid at the beginning of the following year. Another “strategy” of enhancing the liquidity can be the overvaluation of inventories without taking into account the net realizable value.
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An Integrated Approach to Digital Objects Storage and Retrieval

An Integrated Approach to Digital Objects Storage and Retrieval

Abstract: Image retrieval systems are becoming increasingly important in areas of research and commercial use. The storage of digital objects in the traditional databases is considered inadequate because of the extensive precise data required for successful retrieval. In addition, the retrieval process has been implemented using content-based image retrieval (CBIR) that relies on retrieving stored images from a collection by comparing low level features (binary form) that are automatically extracted from the images themselves. Data retrieval requires knowledge of attributes stored along with an adequate and flexible query language. For image repositories and retrieval, we noted that the integration of XML technology and case-based reasoning is more efficient and of great benefit in this area. This is mainly because users both in indexing and retrieval processes, tend to use old cases by associating images that reveal similar features. It is also because XML extends the original theory and offers a flexible approach with accurate data modelling and management tools. In this work, we also used fuzzy reasoning to convert the quantitative attributes into qualitative terms for indexing and retrieval.
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An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.

An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.

Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time con- suming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather com- plex. Therefore, in this paper, we propose an effective framework which is designed to seg- ment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the dif- ferent datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art cluster- ing methods.
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Phenotype classification of zebrafish embryos by supervised learning.

Phenotype classification of zebrafish embryos by supervised learning.

Classification using the Matlab Gait-CAD toolbox was shown to be in good correlation with that from experts [28], however more complex defects were not addressed. Another work de- scribes a way to automatically obtain images of zebrafish embryos using a motorized micro- scope, but leaves classification into phenotypes manual [29]. Other authors [30] developed an automatic system for data acquisition and embryo analysis in multi-well plates, however exten- sive manual intervention is still needed on a case-by-case basis to produce analysis routines using several image segmentations. More recently, an approach was proposed based on auto- matically acquired images, using a high-throughput microscope, followed by automatic classifi- cation into three basic phenotypes (hatched, unhatched and dead) using an algorithm based on MPEG-7 image descriptors and Support Vector Machines [32]. Our group proposed an auto- matic method to localize points of interest in zebrafish images that were taken manually [33]. Finally, a multi-thread system was presented that can simultaneously process multiple zebra- fish larvae placed in a capillary, coupled to image recognition algorithms that fully automate manipulation of the animals, including their orientation and positioning regions of interest within the microscope’s field of view, however no solution to detect specific defects of the lar- vae is proposed [34, 35]. Recently, a method was described to extract body curvature along the length of an adult, swimming zebrafish from video data from a dorsal view {Cheng, 2014 #4248}. Another study describes automatic data acquisition of transgenic fluorescent larvae coupled to extraction of the body length as a single end-point for rapid assessment of a chemi- cal’s toxicity {Lantz-McPeak, 2014 #4253}. Taken together, these studies essentially illustrate the difficulty to consider more specific and complex phenotypes. It clearly appears that the aim of a fully automatic procedure to capture images of zebrafish larvae and classify/quantify their phenotypes is not yet reached [6]. Existing solutions for image capture have to be adapted for high throughput applications, usability and cost-effectiveness. Defect recognition capabilities remain restricted, either by the narrow number of basic phenotypes considered, by the manual intervention still required for image classification or by a weak validation performed on a very small number of samples and/or image acquisition sessions.
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Multimodal classification of remote sensing images

Multimodal classification of remote sensing images

the HS image is analyzed by the spectral signature of each pixel; (3) feature extrac- tion, feature vectors are extracted from the segmented regions of VHS using various descriptors and the spectral signatures are obtained by different dimensionality reduc- tion methods; (4) training, using different learning methods and the feature vectors extracted from both domains a set of base classifiers is created; (5) dynamic weight matrix construction, using the trained base classifiers and a validation set is create a matrix of weights which represents the importance of each classifier at decision in every class; (6) prediction, given unseen samples and built the dynamic weight matrix, a pre- dict for every new sample is made using the weights of the decisions of every classifier at that sample. Our approach has the ability to exploit these classifiers which have a specialty in some specifics classes, but would be suppressed by the other classifiers in an equal weight scheme. The second approach is a boosting-based approach based on the SAMME Adaboost [Zhu et al., 2009]. Such as the Dynamic Majority Vote, the boosting-based approach uses a supervised learning framework, but may be divided in five steps: (1-3) data acquisition, object representation and feature extraction as described in the previous description; (4) training, using the features extracted from both domains and diverse learning methods a set of weak learners is created, which at every boosting iteration once is selected to compose the final strong classifier; (5) prediction, given the unseen samples and the set of selected weak learners, a predict for every new sample is made regarding to the linear combination of the weak learner predictions. In this approach, we exploit the inherent feature selection of the Adaboost for the combination of different modalities, as a natural process.
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Fuzzy Controller for Matrix Converter System to Improve Its Quality of Output

Fuzzy Controller for Matrix Converter System to Improve Its Quality of Output

The voltage output is greater than the previous method. For the values q>0.866, the mean output voltage no longer equals the target output voltage in each switching interval. This inevitably leads to low frequency distortion in the output voltage and /or the input current compared to other methods with q<0.866.For q<0.866, the indirect method yields very similar results to the direct methods.

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Sleep stage classification based on cardiorespiratory signals

Sleep stage classification based on cardiorespiratory signals

Feature processing is a very important step in the training/classification process. This is a preliminary phase which transforms the properties of the data which will be more ef- fectively discriminated by the classifier. The stages in a pattern recognition system are in a pipeline fashion meaning that each step depends on the success of the previous phase in order to produce optimal results. In order to improve the discriminatory power of each feature, it will first normalized. This method aims at minimizing the impact of between- subject variability on the expression of the differences between sleep stages on the fea- tures. After features are normalized, they will be further processed (or transformed). This method aims at extracting additional information about each feature, for example, about the way it varies in time, or its low- or high-frequency components. In order to determine which normalization and transformation methods actually contribute towards these goals, features will be evaluated individually by their discriminatory power using a class sepa- rability measure.
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Ampliação, caracterização fenotípica e estruturação da Coleção de Bactérias aeróbias formadoras de endósporos nativas do solo do Distrito Federal

Ampliação, caracterização fenotípica e estruturação da Coleção de Bactérias aeróbias formadoras de endósporos nativas do solo do Distrito Federal

Aerobic endospore-forming bacteria (AEFB) are both taxonomically and physiologically diverse group comprising species of genus Bacillus and related genera. The usefulness of a great number and risk of others make the demarcation of species in industrially and medically important strains essential. MALDI-TOF MS is widely used in bacterial analyses because of its potential to produce simple and reproducible spectral patterns, which are compared to spectra deposited in commercial databases, mainly composed of clinical isolates. In this work, we evaluated the efficacy of MALDI-TOF MS to identify 64 AEFB strains isolated from Brazilian soils, quoted here as SDF (Solo do Distrito Federal). To further assist the classification of the SDF strains identification based on sequences of 16S rRNA gene were performed in parallel. MALDI-TOF MS and 16S rRNA gene sequences identified 31 (48.43%) and 60 (93.75%) strains at the species level, respectively. Among those identified at the species level, 19 (29.68%) presented the same identification by both technics. However, other 10 predictions revealed by 16S rRNA gene sequences could indicate two or more species alternatives within the same affiliation group and thus could match the same classification obtained by MALDI- TOF MS. Although the discriminatory powers of both methods at the species level are low, our results show that MALDI-TOF MS and 16S rRNA gene sequences are both useful to classify AEFB in a complementary way. This work also reinforces the need to generate free databases built from mass spectra, including environmental samples, and emphasizes the importance of the polyphasic characterization.
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A Comparative Analysis of Image Fusion Techniques for Remote Sensed Images

A Comparative Analysis of Image Fusion Techniques for Remote Sensed Images

The first step in registering two images is to decide on the feature space to use for matching. The wavelets or wavelet- like features are used as the feature space for the registration process. Fig.1 summarizes the adopted registration scheme when wavelet or wavelet-like information is utilized. Both the reference and input images are first decomposed following a multi-resolution wavelet or frame decomposition. In order to achieve computational efficiency, search strategy follows the multi-resolution decomposition, working iteratively from the deepest level of decomposition (where the image size is the smallest) to the top level of decomposition, i.e., going from coarse to fine spatial resolution. For all levels of decomposition, the similarity measure between sub-band images of the reference image and input image is successively computed and maximized. The accuracy of this search increases when going from coarse resolution to fine resolution [2]. Steerable Simoncelli filters [3] are more robust to translation, rotation and noise than the standard Daubechies wavelet filters and so they are adopted to extract the feature space for matching. The method described by Simoncelli enables one to build translation and rotation-invariant filters by relaxing the critical sampling condition of the wavelet transforms.
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Braz. J. Phys.  vol.34 número2A

Braz. J. Phys. vol.34 número2A

In this paper we review our main results on the algebraic classification, limits and their relation to Segre types in 5– D, and algebraic properties of second order symmetric ten- sors (energy momentum, Einstein and Ricci) in 5–D space- times [5] – [8]. We note that the proof of Theorem 1, the consequent treatment of the classification in Segre types and the corresponding canonical forms presented here are new. We also show how one can obtain, by induction, the classifi- cation and the canonical forms of a symmetric two-tensor on n-dimensional (n > 5) spaces from its classification in 5-dimensional spaces. This classification of symmetric two-tensors in any n-dimensional spaces and their canonical forms are important in the context of n-dimensional brane- worlds context as well as in the framework of 11–D super- gravity and 10–D superstrings.
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