Book ChapterDOI
Hyperspectral Image: Fundamentals and Advances
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TLDR
The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral image enhancement, denoising and restoration, classical classification techniques and the most recently popular classification algorithm.Abstract:
Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications. It can capture hyperspectral image (HSI) with a lager number of land-cover information. With the increasing industrial demand in using HSI, there is a must for more efficient and effective methods and data analysis techniques that can deal with the vast data volume of hyperspectral imagery. The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral images. The hyperspectral image enhancement, denoising and restoration, classical classification techniques and the most recently popular classification algorithm are discussed with more details. Besides, the standard hyperspectral datasets used for the research purposes are covered in this chapter.read more
Citations
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Journal ArticleDOI
TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification
TL;DR: Wang et al. as discussed by the authors proposed a triple-attention guided residual dense and BiLSTM networks (TARDB-Net) to reduce redundant features while increasing feature fusion capabilities, which ultimately improves the ability to classify hyperspectral images.
Journal ArticleDOI
Characterizing building materials using multispectral imagery and LiDAR intensity data
TL;DR: In this paper, a Partial Least Squares Discriminant Analysis (PLSA) model was developed to classify the main materials and then the subcategories within each material type.
Journal ArticleDOI
3D modified wavelet block tree coding for hyperspectral images
TL;DR: A novel wavelet-based efficient hyperspectral image compression scheme for low memory sensors that uses the 3D dyadic wavelet transform to exploit intersubband and intrasubband correlation among the wavelet coefficients.
Journal ArticleDOI
Remote sensing image super-resolution based on convolutional blind denoising adaptive dense connection
Proceedings Article
Application of Least Square Denoising to Improve ADMM Based Hyperspectral Image Classification
TL;DR: This paper is presenting a new approach for denoising hyperspectral images based on Least Square Regularization, and it is observed that the proposed Le least square Denoising method improves classification accuracy much better than other existing denoised techniques.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.
Journal ArticleDOI
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Book
Support Vector Machines
TL;DR: This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications and provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature.