Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
نویسندگان
چکیده
منابع مشابه
Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging
Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in ...
متن کاملStructured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging.
Presented in a three-dimensional structure called a hypercube, hyperspectral imaging suffers from a large volume of data and high computational cost for data analysis. To overcome such drawbacks, principal component analysis (PCA) has been widely applied for feature extraction and dimensionality reduction. However, a severe bottleneck is how to compute the PCA covariance matrix efficiently and ...
متن کاملLocal Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In thi...
متن کاملGaussian Processes Autoencoder for Dimensionality Reduction
Learning low dimensional manifold from highly nonlinear data of high dimensionality has become increasingly important for discovering intrinsic representation that can be utilized for data visualization and preprocessing. The autoencoder is a powerful dimensionality reduction technique based on minimizing reconstruction error, and it has regained popularity because it has been efficiently used ...
متن کاملWavelet-Based Dimensionality Reduction for Hyperspectral THz Imaging
With terahertz time-domain spectroscopy, hyperspectral images can be acquired where each pixel contains a full spectrum of the range of several terahertz (THz). An enormous amount of data is generated. Therefore, advanced methods for automated data analysis and image processing are required. We present a wavelet-based approach for channel reduction and feature selection for a subsequent cluster...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2016
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2015.11.044