Showing 1-25 of 15,838 resultsfor "Index Terms":Spectral Set
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Hyperspectral Remote Sensing Rock and Mineral Spectral Feature Mining Based on Rough Set Theory
2008 International Conference on Computer Science and Software Engineering
Year: 2008 | Conference Paper |
The hyperspectral RS is a new technology for fine spectral feature, but the spectral feature of same class object are still not completely consistent, because the ground surface environment is complex. In this paper, we analyze the spectral characteristics and its fractal characteristics, and construct a spectrum curve feature matrix, which consist of center distance, informational entropy, fracta...Show More
Uncertainty Bounds for Spectral Estimation
The purpose of this paper is to study metrics suitable for assessing uncertainty of power spectra when these are based on finite second-order statistics. The family of power spectra which is consistent with a given range of values for the estimated statistics represents the uncertainty set about the “true” power spectrum. Our aim is to quantify the size of this uncertainty set using suitable notio...Show More
Spectral Method for Analysis of Switching Diffusions
The analysis problem of switching diffusions is considered. This paper presents a new approach based on the spectral method formalism for solving generalized Fokker–Planck equations. The proposed method allows to transform partial differential equations into the linear algebraic equations, and to arrive at a solution in an explicit form. The aspects of applications are discussed. A numerical examp...Show More
Fuzzy Spectral and Spatial Feature Integration for Classification of Nonferrous Materials in Hyperspectral Data
Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as ...Show More
The Use of Factorization and Multimode Parametric Spectra in Estimating Frequency and Spectral Parameters of Signal
Valerii Semenets;Vladimir Kartashov;Oleg Sergiyenko;Vyacheslav Tikhonov;Paolo Mercorelli;Sergiy Sheiko;Nataliya Chmelarova Kudriavtseva;Julio C. Rodríguez-Quiñonez;Wendy Flores-Fuentes
2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)
Year: 2020 | Conference Paper |
A multiplicative autoregressive model is constructed based on the linear prediction model. The expressions describing such a model are derived. A formula for the parametric spectral estimation of a random signal multiplicative model is given. Relations are obtained for the decomposition of the multimode power spectral density into simple spectral components. Examples of the decomposition of multim...Show More
The Use of Factorization and Multimode Parametric Spectra in Estimating Frequency and Spectral Parameters of Signal
Valerii Semenets;Vladimir Kartashov;Oleg Sergiyenko;Vyacheslav Tikhonov;Paolo Mercorelli;Sergiy Sheiko;Nataliya Chmelarova Kudriavtseva;Julio C. Rodríguez-Quiñonez;Wendy Flores-Fuentes
Year: 2020 | Conference Paper |
On spectral analysis with nonuniform frequency resolution of nonstationary stochastic processes
2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)
Year: 2012 | Conference Paper |
Spectral analysis with nonuniform frequency resolution of nonstationary stochastic processes is addressed. The frequency-warping operation aimed at increasing the frequency resolution is shown to modify the nonstationarity kind of the analyzed process. Specifically, in several cases of interest, the frequency-warped process is shown to belong to the recently introduced class of the spectrally corr...Show More
A Spectral Feature Selection Approach With Kernelized Fuzzy Rough Sets
Feature evaluation is an important issue in constructing a feature selection algorithm in kernelized fuzzy rough sets, which has been proven to be an effective approach to deal with nonlinear classification tasks and uncertainty in learning problems. However, the feature evaluation function developed with kernelized fuzzy rough sets cannot better reflect the affinity relationship of samples and is...Show More
A Multiscale Spatial–Spectral Prototypical Network for Hyperspectral Image Few-Shot Classification
Due to the complex environment of hyperspectral image (HSI) gathering area, it is difficult to obtain a large number of labeled samples for HSI. Therefore, how to effectively achieve the HSI few-shot classification is a hot spot of current research. Prototypical network (PN) is one of the most classical few-shot learning algorithms, which has been widely employed for few-shot image classification ...Show More
Variable-Number Variable-Band Selection for Feature Characterization in Hyperspectral Signatures
This paper presents a novel band selection-based feature characterization technique for a hyperspectral signature, which is referred to as variable-number variable-band selection (VNVBS). Since a hyperspectral signature can be uniquely characterized by its spectral profile, its feature characterization can be achieved by selecting appropriate bands from the original set of spectral bands, and the ...Show More
Level set segmentation of hyperspectral images using joint spectral edge and signature information
2008 11th International Conference on Information Fusion
Year: 2008 | Conference Paper |
Cited by: Papers (1)
This paper describes a new method for segmenting hyperspectral imagery (HSI) using dynamic curves. We are concerned about challenging HSI target segmentation/detection use cases where the scene includes confusers exhibiting a spectral return similar to the desired signature and in close proximity of the object of interest. Our method is based on a level sets approach. It fuses all available spectr...Show More
Time and Spectral Domain Relative Entropy: A New Approach to Multivariate Spectral Estimation
The concept of spectral relative entropy rate is introduced for jointly stationary Gaussian processes. Using classical information-theoretic results, we establish a remarkable connection between time and spectral domain relative entropy rates. This naturally leads to a new spectral estimation technique where a multivariate version of the Itakura-Saito distance is employed. It may be viewed as an e...Show More
Multiscale 2-D Singular Spectrum Analysis and Principal Component Analysis for Spatial–Spectral Noise-Robust Feature Extraction and Classification of Hyperspectral Images
Ping Ma;Jinchang Ren;Huimin Zhao;Genyun Sun;Paul Murray;Jiangbin Zheng
In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss from removed bands. To tackle these issues, in this article, we propose a novel spatial-spectral feature extraction framework, multiscale 2-D singular s...Show More
RepCPSI: Coordinate-Preserving Proximity Spectral Interaction Network With Reparameterization for Lightweight Spectral Super-Resolution
Existing remarkable models for spectral super-resolution (SSR) achieve higher precision at the expense of computations with larger parameters. These algorithms require the heavy memory footprint and sufficient computing power, limiting their practical deployments and applications on portable devices. In this article, we propose an efficient reparameterizing coordinate-preserving proximity spectral...Show More
Improving Spectral Clustering Using Spectrum-Preserving Node Aggregation
2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022 | Conference Paper |
Cited by: Papers (11)
Spectral clustering is an important graph clustering technique. However, it suffers from a scalability problem due to the involved computationally expensive eigen-decomposition procedure and is highly sensitive to noisy nodes in the graph. In this work we solve the two problems simultaneously by using spectrum-preserving node aggregation to generate a nearly-noiseless concise representation of the...Show More
Spectral efficiency of fiber-optic data transfer systems: Four modifications
2018 Systems of Signals Generating and Processing in the Field of on Board Communications
Year: 2018 | Conference Paper |
Cited by: Papers (2)
The fiber-optic data transfer systems spectral efficiency factor widening conception is proposed. Two kinds of signal transmission rate, - bitrate and baudrate, - and two kinds of spectrum definitions, - frequency and wavelength, - are being analyzed associated with the spectral efficiency factor. Four modifications of spectral efficiency are designed based on four pair transmission rate - spectru...Show More
Mean-Square Consistent Estimation of the Spectral Correlation Density for Spectrally Correlated Stochastic Processes
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
Year: 2007 | Conference Paper |
Cited by: Papers (8)
In this paper, the problem of estimating the spectral correlation density of spectrally correlated stochastic processes is addressed. These processes have Loeve bifrequency spectrum with spectral masses concentrated on a countable set of support curves in the bifrequency plane. The almost-cyclostationary processes are obtained as a special case when the support curves are lines with unit slope. Sp...Show More
Research of Uninformative Variable Elimination method for spectral data analysis of milk
Wang Lijie;Guo Jianying;Ding Xibo;Zhou Zhen;Bai Yamei;Qin Yong
Proceedings of 2011 6th International Forum on Strategic Technology
Year: 2011 | Conference Paper |
During the NIR spectral analysis to quickly determine concentrations of essential components of milk, spectral region is wider, peaks are overlap, and searching space is larger, spectrum acquiring often subjects to interference coming from environmental noise and interference of other components, so it is necessary to make optimum selecting to wavelength variables. In this paper, for concentration...Show More
Towards Spectral Estimation from a Single RGB Image in the Wild
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (28)
In contrast to the current literature, we address the problem of estimating the spectrum from a single common trichromatic RGB image obtained under unconstrained settings (e.g. unknown camera parameters, unknown scene radiance, unknown scene contents). For this we use a reference spectrum as provided by a hyperspectral image camera, and propose efficient deep learning solutions for sensitivity fun...Show More
Information Content Analysis on the Spectral Range 350-2500NM Using Spectral Convolution and PCA
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Year: 2021 | Conference Paper |
Imaging spectroscopy gains more importance in various and diverse applications. Using the appropriate spectral resolution of a spectral sensor for the targeted study area is important to obtain meaningful data. In this study, a Principal Component Analysis (PCA) was performed on spectrally convolved spectral ground control points of different land cover types obtained using field spectroradiometer...Show More
Effective RTL Method to Develop On-Line Self-Test Routine for the Processors Using the Wavelet Transform
Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
Year: 2008 | Conference Paper |
In this paper, we introduce a new efficient register transfer level (RTL) method to develop on-line self- test routines. We consider some prioritizations to select the components and instructions of the processor. In addition, we choose test patterns based on spectral RTL test pattern generation (TPG) strategy. For the purpose of spectral analysis, we use the wavelet transform. Also, we use a few ...Show More
High-Fidelity Hyperspectral Snapshot of Physical World: System Architecture, Dataset and Model
Erqi Huang;Maoqi Zhang;Zhan Ma;Linsen Chen;Yiyu Zhuang;Xun Cao
It is extremely challenging to acquire high-fidelity video of physical world at high spectral, high spatial and high temporal resolution (H$^{3}$R) granularities simultaneously. Existing hyperspectral scanning cameras offer groundtruth with sufficient spatiospectral resolutions but largely lack temporal details; while recent hyperspectral snapshot cameras (e.g., CASSI, PMVIS) enable high temporal ...Show More
Spectral–Spatial Classification of Hyperspectral Imagery Based on Moment Invariants
This paper presents a novel and efficient spectral–spatial classification method for hyperspectral images. It combines the spectral and texture features to improve the classification accuracy. The moment invariants are computed within a small window centered at the pixel to determine pixel-wise texture features. The texture and spectral features are concatenated to form a joint feature vector that...Show More
Statistics of hyperspectral data/image analysis: Entropy
Yu-Jung Chen;Noël Richard;Hilda Deborah;Aurélie Tournié;Anne Michelin;Christine Andraud
2018 Colour and Visual Computing Symposium (CVCS)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
In this work, we are focusing on the development of metrological features for the analysis of hyperspectral measurements. We propose to define the entropy of a hyperspectral dataset or image. As the processing cannot be developed in the spectral acquisition space, the proposed entropy is processed using histograms of spectral differences. The metrological properties are induced by an adapted spect...Show More
Level Set Hyperspectral Image Classification Using Best Band Analysis
We present a supervised hyperspectral classification procedure consisting of an initial distance-based segmentation method that uses best band analysis (BBA), followed by a level set enhancement that forces localized region homogeneity. The proposed method is tested on two hyperspectral images of an urban and rural nature. The proposed method is compared to the maximum likelihood (ML) method using...Show More
Set-to-Set Distance-Based Spectral–Spatial Classification of Hyperspectral Images
Ting Lu;Shutao Li;Leyuan Fang;Lorenzo Bruzzone;Jón Atli Benediktsson
A novel set-to-set distance-based spectral-spatial classification method for hyperspectral images (HSIs) is proposed. In HSIs, the spatially connected and spectrally similar pixels within each homogeneous region can be considered as one set of test samples, i.e., a test set, which should belong to the same class. In addition, each class of labeled pixels can be regarded as one set of training samp...Show More
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