Book ChapterDOI
Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications
R S Vaddi,R S Vaddi,Prabukumar Manoharan +2 more
- pp 863-869
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TLDR
This work adopted structure-preserving recursive filter to noise removal and Probabilistic based principal component analysis is applied to reduce dimensionality and obtained results show that the proposed HSI Classification method provides results on par with similar type of methods from literature.Abstract:
Hyper spectral image (HSI) Classification has become important research areas of remote sensing which can be used in many practical applications, including precision agriculture, Land cover mapping, environmental monitoring etc. HSI Classification includes various steps like Noise removal, dimensionality reduction, and classification. In this work, we adopted structure-preserving recursive filter (SPRF) to noise removal and Probabilistic based principal component analysis (PPCA) is applied to reduce dimensionality. Finally classification is performed using multi class large marginal distribution machine (LDM). The proposed (HSI) Classification method is carried out and results are validated across the three widely used standard datasets like Indian Pines, University of Pavia and Salinas. The obtained results show that the proposed method provides results on par with similar type of methods from literature.read more
Citations
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Journal ArticleDOI
Hyperspectral image classification using CNN with spectral and spatial features integration
R S Vaddi,Prabukumar Manoharan +1 more
TL;DR: This paper presents an approach for remote sensing hyper spectral image classification based on data normalization and CNN, which has achieved significant performance over the state-of-art methods.
Journal ArticleDOI
New framework for hyperspectral band selection using modified wind-driven optimization algorithm
TL;DR: A modified WDO (MWDO) is proposed for band selection, which is able to avoid the premature convergence and control the exploration–exploitation search trade-off and provides high classification accuracy with fewer bands in comparison with other approaches.
Journal ArticleDOI
Hyperspectral band selection based on metaheuristic optimization approach
TL;DR: The experimental results on two standard benchmark datasets, Pavia University and Indian Pines, prove the superiority of the proposed method over standard CS approach as well as the other traditional approaches in terms of average accuracy, overall accuracy, Cohen’s kappa coefficient (κ), statistical significance assessment using McNemar's test, and fitness curve analysis.
Journal ArticleDOI
A new framework for hyperspectral image classification using Gabor embedded patch based convolution neural network
TL;DR: This research work, initially spatio-spectral features are fused by extracting the uncorrelated bands and exploit the texture patterns via exploratory factor analysis and Gabor filter respectively and embedded these features to the original cube underlying the assumption that the noise is heteroscedastic in each of the variable in factor analysis.
Journal ArticleDOI
A survey of band selection techniques for hyperspectral image classification
TL;DR: An extensive and comprehensive survey on band selection techniques for hyperspectral image classification is provided describing the categorisation of methods, methodology used, different searching approaches and various technical difficulties, as well as their performances.
References
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Journal ArticleDOI
Probabilistic Principal Component Analysis
TL;DR: In this paper, the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis.
Journal ArticleDOI
Deep Learning-Based Classification of Hyperspectral Data
TL;DR: The concept of deep learning is introduced into hyperspectral data classification for the first time, and a new way of classifying with spatial-dominated information is proposed, which is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression.
Journal ArticleDOI
Precision agriculture—a worldwide overview
TL;DR: In this article, the authors provide an overview of worldwide development and current status of precision-agriculture technologies based on literatures generated mainly during the past two years, including natural resource variability; variability management; management zone; impact of precision agriculture technologies on farm profitability and environment; engineering innovations in sensors, controls, and remote sensing; information management; worldwide applications and adoption trend of precision agricultural technologies; and potentials of the technologies in modernizing the agriculture in China.
Proceedings ArticleDOI
Domain transform for edge-aware image and video processing
TL;DR: The use of 1D operations leads to considerable speedups over existing techniques and potential memory savings; its computational cost is not affected by the choice of the filter parameters; and it is the first edge-preserving filter to work on color images at arbitrary scales in real time, without resorting to subsampling or quantization.
Journal ArticleDOI
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering
TL;DR: Compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency.
Related Papers (5)
Unsupervised band selection based on weighted information entropy and 3D discrete cosine transform for hyperspectral image classification
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