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QUALITY ASSESSMENT OF DIMENSIONALITY REDUCTION TECHNIQUES ON HYPERSPECTRAL DATA: A NEURAL NETWORK BASED APPROACH

C. Deepa, A. Shetty, A. V. Narasimhadhan
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Dimensionality reduction of hyperspectral images plays a vital role in remote sensing data analysis.  ...  Limited studies have been carried out on dimensionality reduction for mineral exploration.  ...  NEURAL NETWORK BASED DIMENSIONALITY REDUCTION In the past few years, neural network based approaches for classifying hyperspectral data received a lot of attention (Merenyi, 2005) .  ... 
doi:10.5194/isprs-archives-xliii-b3-2020-389-2020 fatcat:c5b5kfw6wvbh3ncw7la7wppomi

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Yang 597 Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image .................................................................................  ...  436 Hyperspectral 436 Data Residual Spectral-Spatial Attention Network for Hyperspectral Image Classification ...................................... ....................................................  ... 
doi:10.1109/tgrs.2020.3039449 fatcat:sxee5msl55emhnszejgpf6vrpa

Special Section Guest Editorial: Satellite Hyperspectral Remote Sensing: Algorithms and Applications

Kun Tan, Xiuping Jia, Antonio Plaza
2021 Journal of Applied Remote Sensing  
Wang et al. introduced a pyramidal self-attentive mechanism in deep neural networks for change detection and validated using the hyperspectral change detection dataset from ZY1.  ...  Wang et al. used a dimension reduction method based on the variable importance projection (VIP) and segmented principal component analysis (SPCA) method for the water quality parameters retrieval from  ...  These unique studies contributed to the development of satellite hyperspectral technology and laid the foundation for hyperspectral applications in a variety of fields.  ... 
doi:10.1117/1.jrs.15.042601 fatcat:ksl6q5deebdv3fmgmf2b2i6wym

A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples

Sen Jia, Shuguo Jiang, Zhijie Lin, Nanying Li, Meng Xu, Shiqi Yu
2021 Neurocomputing  
With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification.  ...  For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.  ...  Fig. 2 . 2 The category of deep learning-based methods for hyperspectral image classification.  ... 
doi:10.1016/j.neucom.2021.03.035 fatcat:jkufaor2jnbcvei5ndhqukrhoy

Multiscale Dense Cross-Attention Mechanism with Covariance Pooling for Hyperspectral Image Scene Classification

Runmin Liu, Xin Ning, Weiwei Cai, Guangjun Li
2021 Mobile Information Systems  
In recent years, learning algorithms based on deep convolution frameworks have gradually become the research hotspots in hyperspectral image classification tasks.  ...  Therefore, this paper investigates data dimensionality reduction and feature extraction and proposes a novel multiscale dense cross-attention mechanism algorithm with covariance pooling (MDCA-CP) for hyperspectral  ...  the focus of research in the field of hyperspectral remote sensing. e classic neural network models based on deep learning include the autoencoder (AE) [22] , stacked autoencoder (SAE) [23] , and restricted  ... 
doi:10.1155/2021/9962057 doaj:fe0b8170afd44a0da84b35c6a278c08f fatcat:w3opirm7nzezpgp6xztmdwopr4

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature.  ...  Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  The PCA is primarily used for dimensionality reduction in hyperspectral images.  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification [article]

Zeyu Cao, Xiaorun Li, Liaoying Zhao
2020 arXiv   pre-print
As a result, our method surpasses other comparison methods in the hyperspectral classification experiments, including some supervised methods.  ...  hyperspectral classification.  ...  Recent years, many great supervised deep learning methods for hyperspectral classification were proposed, such as one-dimensional convolutional neural networks(1DCNN) [1] , three-dimensional convolutional  ... 
arXiv:2009.00953v1 fatcat:adwd6djgsjeo5fjv4maejgzs7m

Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification

Wenmei Li, Huaihuai Chen, Qing Liu, Haiyan Liu, Yu Wang, Guan Gui
2022 Remote Sensing  
Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing.  ...  However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images.  ...  Acknowledgments: We would like to thank the anonymous reviewers and the editor-in-chief for their comments to improve the paper. Thanks also to the data sharer.  ... 
doi:10.3390/rs14092215 fatcat:kc3okdha6rhipkfewovzwqawpq

Table of contents

2020 IEEE Geoscience and Remote Sensing Letters  
Torres 1450 Fully Convolutional Siamese Autoencoder for Change Detection in UAV Aerial Images ................................. ........................ D. B. Mesquita, R. F. dos Santos, D. G.  ...  Zhang 1420 Unsupervised Dimensionality Reduction for Hyperspectral Imagery via Local Geometric Structure Feature Learning ...............................................................................  ... 
doi:10.1109/lgrs.2020.3006659 fatcat:ewiznjupqvcfzkoj6atbtr5nae

CAEVT: Convolutional Autoencoder Meets Lightweight Vision Transformer for Hyperspectral Image Classification

Zhiwen Zhang, Teng Li, Xuebin Tang, Xiang Hu, Yuanxi Peng
2022 Sensors  
Moreover, as traditional dimensionality reduction methods are limited in their linear representation ability, a three-dimensional convolutional autoencoder was adopted to capture the nonlinear characteristics  ...  Convolutional neural networks (CNNs) have been prominent in most hyperspectral image (HSI) processing applications due to their advantages in extracting local information.  ...  autoencoder (3D-CAE) network for nonlinear feature dimensionality reduction.  ... 
doi:10.3390/s22103902 fatcat:xfkc7ugcvbhq7asylxbr25cc3a

A study of Multispectral Technology and Two-dimension Autoencoder for Coal and Gangue Recognition

Wenhao Lai, Mengran Zhou, Feng Hu, Kai Bian, Hongping Song
2020 IEEE Access  
Also, the accuracy of gangue recognition is different for spectral images of different wavelengths.  ...  Secondly, design a spectral data dimension reduction model called two-dimension autoencoder(2D-AE). Finally, Random Forest was used to recognize coal and gangue.  ...  Therefore, this study designs an unsupervised learning model called two-dimensional autoencoder (2D-AE) based on CNN, which is only used for spectral image dimension reduction.  ... 
doi:10.1109/access.2020.2983740 fatcat:czco2vqwyrcwlmn2yzaqhdohme

Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing

Ziqiang Hua, Xiaorun Li, Jianfeng Jiang, Liaoying Zhao
2021 Remote Sensing  
Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing.  ...  In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process.  ...  Data Availability Statement: The data used in this study are available at https://www.kaggle.com/ ziqhua/hyperspectral-images (accessed on 23 April 2021).  ... 
doi:10.3390/rs13163147 fatcat:snphfepgn5af5pxbjnb2dhqye4

A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification

Jinling Zhao, Lei Hu, Yingying Dong, Linsheng Huang, Shizhuang Weng, Dongyan Zhang
2021 International Journal of Applied Earth Observation and Geoinformation  
Considering the rich spectral and spatial information contained in hyperspectral images (HSIs), a combination method was proposed for HSI classification based on stacked autoencoder (SAE) and 3D deep residual  ...  In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images.  ...  A SAE neural network was then built to train the normalized image for performing dimensionality reduction.  ... 
doi:10.1016/j.jag.2021.102459 fatcat:aejpjmdfqvfg5e3pqizclm4chu

Learning Based Super Resolution Application for Hyperspectral Images

Hüseyin AYDİLEK, Nihat İNANÇ
2021 International scientific and vocational studies journal  
In this paper, a hybrid application based on deep learning and sparse representation is applied to increase the low spatial resolution of hyperspectral images.  ...  First the application obtains a super-resolution image from a single hyperspectral image with a low spatial image with a deep convolutional neural network.  ...  For this purpose, a 2-dimensional convolutional neural network-based structure was used in the first part of the study.  ... 
doi:10.47897/bilmes.1049338 fatcat:wayekvbxkngn7mn4yhwxvocpqi

Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios [chapter]

Xiong Zhou, Saurabh Prasad
2020 Advances in Computer Vision and Pattern Recognition  
In this chapter, we will review recent advances in the community that leverage deep learning for robust hyperspectral image analysis despite these unique challenges -- specifically, we will review unsupervised  ...  In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks.  ...  In traditional feature learning (e.g. dimensionality reduction, subspace learning or spatial feature extraction), the processing operators are often based on assumptions or prior knowledge about data characteristics  ... 
doi:10.1007/978-3-030-38617-7_5 fatcat:23ibk4ojbvepbpikxgjxan4i6e
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