Superpixelwise Collaborative-Representation Graph Embedding for Unsupervised Dimension Reduction in Hyperspectral Imagery

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Unsupervised Locally Linear Embedding for Dimension Reduction

In this paper, Locally Linear Embedding (LLE) has been implemented for unsupervised non-linear dimension reduction that computes low dimensional, neighborhood preserving embeddings of high dimensional data. Inputs are mapped into a single global coordinate system of lower dimensionality, and its optimizations though capable of generating highly nonlinear embeddings but local minima are not invo...

متن کامل

A New Dictionary Construction Method in Sparse Representation Techniques for Target Detection in Hyperspectral Imagery

Hyperspectral data in Remote Sensing which have been gathered with efficient spectral resolution (about 10 nanometer) contain a plethora of spectral bands (roughly 200 bands). Since precious information about the spectral features of target materials can be extracted from these data, they have been used exclusively in hyperspectral target detection. One of the problem associated with the detect...

متن کامل

Local Neighborhood Embedding for Unsupervised Nonlinear Dimension Reduction

The construction of similarity relationship among data points plays a critical role in manifold learning. There exist two popular schemes, i.e., pairwise-distance based similarity and reconstruction coefficient based similarity. Existing works only have involved one scheme of them. These two schemes have different drawbacks. For pairwisedistance based similarity graph algorithms, they are sensi...

متن کامل

Unsupervised Kernel Dimension Reduction

We apply the framework of kernel dimension reduction, originally designed for supervised problems, to unsupervised dimensionality reduction. In this framework, kernel-based measures of independence are used to derive low-dimensional representations that maximally capture information in covariates in order to predict responses. We extend this idea and develop similarly motivated measures for uns...

متن کامل

Band reduction for hyperspectral imagery processing

Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation. Reduction can be done by either identifying bands from the original subset (selection), or by employing var...

متن کامل

ذخیره 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: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

سال: 2021

ISSN: 1939-1404,2151-1535

DOI: 10.1109/jstars.2021.3077460