☆ 4.7 Article

Residual Dense Autoencoder Network for Nonlinear Hyperspectral Unmixing

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Electrical & Electronic

Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison

Burkni Palsson et al.

Summary: This article explores the application of deep learning, specifically autoencoders, in remote sensing, focusing on blind hyperspectral unmixing. Through comparative experiments with 11 different autoencoder methods and one traditional method, as well as extensive ablation experiments with a simple spectral unmixing autoencoder, the article unravels the power of autoencoder methods and examines the impact of implementation details on the results.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

添加到收藏夹

Article Geochemistry & Geophysics

BCUN: Bayesian Fully Convolutional Neural Network for Hyperspectral Spectral Unmixing

Yuan Fang et al.

Summary: This article presents a Bayesian fully convolutional hyperspectral unmixing network (BCUN) that addresses the challenges in spectral unmixing, such as accurate estimation of noise effect, endmembers, and spatial correlation effect. The proposed BCUN integrates a deep image prior, a Mahalanobis distance-based loss, and a spectral mixture model into a coherent Bayesian framework. Experimental results demonstrate its superior performance compared to other classical and state-of-the-art methods in both endmember estimation and abundance estimation.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral Unmixing for Additive Nonlinear Models With a 3-D-CNN Autoencoder Network

Min Zhao et al.

Summary: This article introduces an unsupervised nonlinear spectral unmixing method that utilizes a deep autoencoder network and convolutional neural network to handle complex hyperspectral data, demonstrating its generality and effectiveness compared to state-of-the-art methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Geochemistry & Geophysics

Deep Autoencoder for Hyperspectral Unmixing via Global-Local Smoothing

Xia Xu et al.

Summary: This article introduces a deep network for hyperspectral unmixing based on a new global-local smoothing autoencoder (GLA) that aims to explore the local homogeneity and global self-similarity of hyperspectral imagery. The proposed GLA network includes two modules: a Local Continuous conditional random field Smoothing (LCS) module and a global recurrent smoothing (GRS) module, which show promising results in abundance estimation compared to state-of-the-art unmixing methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Geochemistry & Geophysics

Deep Autoencoders With Multitask Learning for Bilinear Hyperspectral Unmixing

Yuanchao Su et al.

Summary: Hyperspectral unmixing is a challenging problem that requires detailed knowledge of physical interactions, but the proposed method using bilinear mixture models and deep autoencoders shows competitive performance in estimating nonlinear interactions in real scenarios.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

添加到收藏夹

Article Geochemistry & Geophysics

Physically Constrained Transfer Learning Through Shared Abundance Space for Hyperspectral Image Classification

Ying Qu et al.

Summary: Hyperspectral image (HSI) classification is an active research topic, and recent advances in deep learning have greatly improved its performance. Transfer learning-based methods can help address the issue of domain discrepancies between training and testing images, and the proposed physically constrained transfer learning through shared abundance space (PCTL-SAS) method demonstrates superior results compared to state-of-the-art approaches. This new method has the potential to facilitate the deployment of HSI classification in real-world scenarios.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

LSTM-DNN Based Autoencoder Network for Nonlinear Hyperspectral Image Unmixing

Min Zhao et al.

Summary: This paper proposes a nonsymmetric autoencoder network for blind hyperspectral unmixing, which takes advantage of the universal modeling ability of deep neural networks to learn nonlinear relations from data. It includes an LSTM structure and spatial regularization to capture spectral and spatial correlation information, and an attention mechanism to enhance unmixing performance. Experiments validate the effectiveness of the proposed method using synthetic and real data.

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2021)

添加到收藏夹

Article Engineering, Aerospace

Evaluating the VNIR-SWIR datasets of WorldView-3 for lithological mapping of a metamorphic-igneous terrain using support vector machine algorithm; a case study of Central Iran

Sogand Karimzadeh et al.

Summary: This study successfully utilized data from the WorldView-3 satellite and the support vector machine (SVM) classification method to map lithological units in the Chadormalu area of Iran with an accuracy of 88.36%. Results demonstrate that band 7 of the SWIR region significantly improves classification accuracy.

ADVANCES IN SPACE RESEARCH (2021)

添加到收藏夹

Article Agriculture, Multidisciplinary

Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

Yang Fei-fei et al.

Summary: Waterlogging is increasingly affecting food production, with leaf water content (LWC) being an important indicator of waterlogging. Hyperspectral remote sensing is a valuable method to determine LWC, as demonstrated in a study on winter wheat under different waterlogging stress levels. The study found that waterlogging stress leads to a decrease in LWC, with faster decrease under severe stress but a greater effect of long-term slight stress. The best model for determining LWC in winter wheat under waterlogging stress was found to be a BP neural network model based on specific spectral bands.

JOURNAL OF INTEGRATIVE AGRICULTURE (2021)

添加到收藏夹

Article Agriculture, Multidisciplinary

Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

Liu Da-Zhong et al.

Summary: The study proposed a new method for extracting fractional vegetation cover from wheat hyperspectral images using the density peak k-means algorithm, which showed to be more accurate and robust compared to traditional pixel dichotomy methods under various conditions.

JOURNAL OF INTEGRATIVE AGRICULTURE (2021)

添加到收藏夹

Article Environmental Sciences

Gated Autoencoder Network for Spectral-Spatial Hyperspectral Unmixing

Ziqiang Hua et al.

Summary: This paper proposes two gated autoencoder networks that adaptively control the contribution of spectral and spatial features in spectral unmixing, filtering and regularization spatial features through gating mechanism, achieving superior experimental results compared to state-of-the-art techniques.

REMOTE SENSING (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

A Blind Spectral Unmixing in Wavelet Domain

S. S. Vijayashekhar et al.

Summary: A wavelet-based energy minimization framework is developed to jointly estimate endmembers and abundances without assuming pure pixels, while considering noisy scenarios. Experimental results demonstrate that the algorithm performs well on various types of hyperspectral datasets.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Hyperspectral Satellites, Evolution, and Development History

Shen-En Qian

Summary: This article provides an overview of spaceborne hyperspectral imagers, their technology evolution, development history, as well as the current deployment and upcoming launches of hyperspectral imagers. By summarizing the past and future of hyperspectral technology, it highlights the significant progress in earth observation and space exploration enabled by hyperspectral imaging.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

添加到收藏夹

Article Remote Sensing

Hyperspectral unmixing using deep convolutional autoencoder

Menna M. Elkholy et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2020)

添加到收藏夹

Article Environmental Sciences

Attention-Based Residual Network with Scattering Transform Features for Hyperspectral Unmixing with Limited Training Samples

Yiliang Zeng et al.

REMOTE SENSING (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Deep spectral convolution network for hyperspectral image unmixing with spectral library

Lin Qi et al.

SIGNAL PROCESSING (2020)

添加到收藏夹

Article Geochemistry & Geophysics

Tracing Mineralogy and Alteration Intensity Using the Spectral Alteration Index and Depth Ratios at the Northwest Zone of the Lemarchant Volcanogenic Massive Sulfide Deposit, Newfoundland, Canada

Jonathan Cloutier et al.

ECONOMIC GEOLOGY (2020)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

DEEP LEARNING IN HYPERSPECTRAL UNMIXING: A REVIEW

Jignesh S. Bhatt et al.

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Deep-Learning-Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil From Hyperspectral Data

Ajay Kumar Patel et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing

Ricardo Augusto Borsoi et al.

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Virtual Dimensionality of Hyperspectral Data: Use of Multiple Hypothesis Testing for Controlling Type-I Error

Vijayashekhar S S et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2020)

添加到收藏夹

Article Geochemistry & Geophysics

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

Savas Ozkan et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

添加到收藏夹

Article Geochemistry & Geophysics

uDAS: An Untied Denoising Autoencoder With Sparsity for Spectral Unmixing

Ying Qu et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network

Qiangqiang Yuan et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

添加到收藏夹

Article Geochemistry & Geophysics

DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing

Yuanchao Su et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

添加到收藏夹

Article Computer Science, Information Systems

Multiple Feature Reweight DenseNet for Image Classification

Ke Zhang et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Geochemistry & Geophysics

Stacked Nonnegative Sparse Autoencoders for Robust Hyperspectral Unmixing

Yuanchao Su et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2018)

添加到收藏夹

Review Engineering, Electrical & Electronic

A Review of Virtual Dimensionality for Hyperspectral Imagery

Chein-I Chang

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2018)

添加到收藏夹

Article Computer Science, Information Systems

Hyperspectral Unmixing Using a Neural Network Autoencoder

Burkni Palsson et al.

IEEE ACCESS (2018)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral Unmixing via Deep Convolutional Neural Networks

Xiangrong Zhang et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2018)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation

Xin-Ru Feng et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2018)

添加到收藏夹

Article Environmental Sciences

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Chang Li et al.

REMOTE SENSING (2018)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Simon Jegou et al.

2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2017)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Densely Connected Convolutional Networks

Gao Huang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

添加到收藏夹

Article Geochemistry & Geophysics

Parallel Hyperspectral Unmixing Method via Split Augmented Lagrangian on GPU

Jorge Sevilla et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2016)

添加到收藏夹

Article Geochemistry & Geophysics

Multilayer Unmixing for Hyperspectral Imagery With Fast Kernel Archetypal Analysis

Genping Zhao et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2016)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization

Cedric Fevotte et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2015)

添加到收藏夹

Article Engineering, Electrical & Electronic

A Review of Nonlinear Hyperspectral Unmixing Methods

Rob Heylen et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2014)

添加到收藏夹

Article Engineering, Electrical & Electronic

Nonlinear Unmixing of Hyperspectral Images

Nicolas Dobigeon et al.

IEEE SIGNAL PROCESSING MAGAZINE (2014)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm

Yoann Altmann et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2014)

添加到收藏夹

Article Engineering, Electrical & Electronic

Blind nonlinear hyperspectral unmixing based on constrained kernel nonnegative matrix factorization

Xiaorun Li et al.

SIGNAL IMAGE AND VIDEO PROCESSING (2014)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction

Wei Wang et al.

2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2014)

添加到收藏夹

Article Geochemistry & Geophysics

Spectral Unmixing in Multiple-Kernel Hilbert Space for Hyperspectral Imagery

Yanfeng Gu et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2013)

添加到收藏夹

Article Geochemistry & Geophysics

Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model

Abderrahim Halimi et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2011)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Minimum-Volume-Constrained Nonnegative Matrix Factorization: Enhanced Ability of Learning Parts

Guoxu Zhou et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2011)

添加到收藏夹

Article Engineering, Electrical & Electronic

Minimum Mean Square Distance Estimation of a Subspace

Olivier Besson et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2011)

添加到收藏夹

Article Water Resources

Estimating soil moisture using remote sensing data: A machine learning approach

Sajjad Ahmad et al.

ADVANCES IN WATER RESOURCES (2010)

添加到收藏夹

Article Remote Sensing

Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data

Wenyi Fan et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2009)

添加到收藏夹

Proceedings Paper Engineering, Electrical & Electronic

A VARIABLE SPLITTING AUGMENTED LAGRANGIAN APPROACH TO LINEAR SPECTRAL UNMIXING

Jose M. Bioucas-Dias

2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (2009)

添加到收藏夹

Article Geochemistry & Geophysics

Hyperspectral subspace identification

Jose M. Bioucas-Dias et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2008)

添加到收藏夹

Article Geochemistry & Geophysics

Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization

Lidan Miao et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2007)

添加到收藏夹

Article Geochemistry & Geophysics

Vertex component analysis: A fast algorithm to unmix hyperspectral data

JMP Nascimento et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2005)

添加到收藏夹

Article Automation & Control Systems

The successive projections algorithm for variable selection in spectroscopic multicomponent analysis

MCU Araújo et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2001)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.