[PDF] Top 20 Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
Has 10000 "Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging" found on our website. Below are the top 20 most common "Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging".
Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
... spectral-domain feature extraction is widely employed. For effective spatial information extraction, a 2-D extension to singular spectrum analysis (SSA), a recent ... See full document
31
Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
... spectral-domain feature extraction is widely employed. For effective spatial information extraction, a 2-D extension to singular spectrum analysis (SSA), a recent ... See full document
32
Joint bilateral filtering and spectral similarity-based sparse representation : a generic framework for effective feature extraction and data classification in hyperspectral imaging
... proposed classification framework (JSR-SS-JSRC) using the joint bilateral filtering together with the SS-JSRC method is compared with a few state-of-the-art spectral-spatial classification ...spatial ... See full document
22
Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging
... series analysis, singular spectrum analysis (SSA) has been successfully applied for feature extraction in hyperspectral imaging (HSI), leading to increased accuracy ... See full document
9
Singular spectrum analysis for effective feature extraction in hyperspectral imaging
... series analysis, Singular Spectrum Analysis (SSA) has been applied in many diverse areas, where an original 1D signal can be decomposed into a sum of components including varying trends, ... See full document
5
Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
... on feature representation include widely known classical techniques and, on the other hand, more modern ...component analysis (PCA) [5], independent component analysis (ICA) [6], or maximum noise ... See full document
18
Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis
... paper, two powerful tools, SSA and the curvelet transform, are combined for HSI feature ...more effective feature ...HSI data, the proposed method takes advantage of those ...available ... See full document
16
Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing
... large dimensional datasets such as Hyperspectral Imaging ...a novel Folded-PCA is proposed, in which the spectral vector is folded into a matrix to allow the covariance matrix to be determined ... See full document
22
Dimensionality reduction based on determinantal point process and singular spectrum analysis for hyperspectral images
... low dimensional representation of the original data whilst maintain the global structure of the original ...Component Analysis (PCA) [29] and Isometric Feature Mapping (ISOMAP) ...some ... See full document
10
A new kernel method for hyperspectral image feature extraction
... the analysis and interpretation of hyperspectral images a ...challenge. Feature extraction is a very important step for hyperspectral image ...processing. Feature ... See full document
10
Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
... multi-kernel classification [9] (MK), the sparse multinomial logistic regression [10-11] and the extreme learning machine [12-13] ...for feature extraction, such as principal component ... See full document
15
Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images
... multi-kernel classification [9] (MK), the sparse multinomial logistic regression [10-11] and the extreme learning machine [12-13] ...for feature extraction, such as principal component ... See full document
14
Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging
... sensing, it suffers from extremely high computational cost, especially for 2D-SSA. As a result, a fast implementation of 2D-SSA namely F-2D-SSA is presented in this paper, where the computational complexity has been ... See full document
20
Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
... for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as ...analysed ... See full document
9
Spectral Spatial Hyperspectral Image Classification based on Randomized Singular Value Decomposition and 3 Dimensional Discrete Wavelet Transform
... image Classification is one of the most active areas of research and development in the field of hyperspectral image ...original hyperspectral image data is a simple and effective ...A ... See full document
10
Feature Extraction for Document Classification
... Nawei Chen discussed an automatic document classification for organizing and mining the documents. Information in the documents is often conveyed using both text and images that complement each other [5]. ... See full document
7
Feature Extraction for Classification in Knowledge
... Feature extraction is one of the dimensionality reduction techniques that are often used to cope with the problems caused by the “curse of ...eigenvector-based feature extraction approaches, ... See full document
7
Analysis of Lung Nodule Classification with Feature Extraction
... supervised classification method for lung nodule LDCT ...for classification of lung nodules into four categories: juxta-pleural, well-circumscribed, vascularized and pleural-tail, based on the extracted ... See full document
5
Feature Extraction and Image classification
... In the paper[15] it involves two aims that have emerged in neural networks i.e the increase in the ability to understand the behaviour of nervous system and thus taking inspiration from this knowledge and building ... See full document
13
FEATURE SELECTION BOOSTER ALGORITHM FOR HIGH DIMENSIONAL DATA CLASSIFICATION
... four feature selection algorithms as minimum- redundancy- maximal- relevance (mRMR), Fast Correlation Based Filter (FCBF), Fast clustering bAsed feature Selection Algorithm (FAST) and mRMRe is the ensemble ... See full document
11
Related subjects