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Cross-domain CNN for Hyperspectral Image Classification
[article]
2018
arXiv
pre-print
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To cope with this problem, we propose a novel cross-domain CNN containing the shared parameters which can co-learn across multiple hyperspectral datasets. ...
In this paper, we address the dataset scarcity issue with the hyperspectral image classification. ...
CONCLUSION In this paper, we have introduced a novel cross-domain CNN which can concurrently learn and perform the hyperspectral image classification for multiple datasets. ...
arXiv:1802.00093v2
fatcat:f7wc6qz3p5hnzczl4u52p6qade
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation
[article]
2022
arXiv
pre-print
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The proposed framework architecture leverages cross-domain CNN, allowing for learning representations from different hyperspectral images with varying spectral characteristics and no pixel-level annotation ...
This paper introduces a self-supervised learning framework suitable for hyperspectral images that are inherently challenging to annotate. ...
unlabeled hyperspectral images with different spectral properties. ...
arXiv:2202.03968v1
fatcat:k22cxyndi5dxdlnvdgwba4q4yu
Is Pretraining Necessary for Hyperspectral Image Classification?
[article]
2019
arXiv
pre-print
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We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? ...
To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that differ in their hyperspectral characteristics and fine-tunes on a target dataset. ...
Cross-Domain CNN. Lee et al. [2] introduced Cross-Domain CNN which simultaneously trains multiple networks for classifying multiple hyperspectral image domains. ...
arXiv:1901.08658v1
fatcat:2wequy42kbfqvajd4zeairiuia
Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification
2020
Remote Sensing
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Accurate hyperspectral image classification has been an important yet challenging task for years. ...
This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. ...
cross-domain transfer. 3. ...
doi:10.3390/rs12122033
fatcat:eiqyksosx5arxjhxqv5yfrtjgy
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification
2022
IEEE Transactions on Geoscience and Remote Sensing
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2022 pre-print arXiv:2204.03144v1 fatcat:a6pedzcn4rg2xhhvxwyp73cpiq
We seek to train a universal cross-domain model which can later be deployed for various spectral domains. ...
First few layers of a CNN pretrained on a large-scale RGB dataset are capable of acquiring general image characteristics which are remarkably effective in tasks targeted for different RGB datasets. ...
Wonkook Kim at Pusan National University for his help with the experiments. ...
doi:10.1109/tgrs.2022.3165441
fatcat:ndeh7kasbrgs7pqmpv4fy2anse
Sample Generation with Self-Attention Generative Adversarial Adaptation Network (SaGAAN) for Hyperspectral Image Classification
2020
Remote Sensing
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Hyperspectral image analysis plays an important role in agriculture, mineral industry, and for military purposes. ...
The experiment results illustrate that the proposed method can greatly improve the classification accuracy, even with a small number of initial labeled samples. ...
For the purpose of image classification, the 1D CNN framework was applied for hyperspectral image classification. ...
doi:10.3390/rs12050843
fatcat:qiublsnjbrfjxl2ibjqyykmtke
Compressive spectral image classification using 3D coded convolutional neural network
[article]
2021
arXiv
pre-print
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Hyperspectral image classification (HIC) is an active research topic in remote sensing. ...
A new kind of deep learning strategy, namely 3D coded convolutional neural network (3D-CCNN) is proposed to efficiently solve for the classification problem, where the hardware-based coded aperture is ...
The cross mark can be imaged on the detector, and we can adjust the position of the cross-mark image to align the coded aperture with the detector. ...
arXiv:2009.11948v3
fatcat:htukyp6nijdgphnkjl5fsaqg4u
HİPERSPEKTRAL VERİLERİN SINIFLANDIRMASINDA DERİN ÖĞRENME VE BOYUT İNDİRGEME TEKNİKLERİNİN KARŞILAŞTIRILMASI
2018
Uludağ University Journal of The Faculty of Engineering
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The obtained results on hyperspectral image data sets show that our proposed CNN architecture improves the accuracy rates for classification performance, when compared to traditional methods by increasing ...
Recently, deep convolutional neural networks are proposed to classify hyperspectral images directly in the spectral domain. ...
However, CNNs have been mostly used for visual-related problems, a relatively newer method for hyperspectral image classification. ...
doi:10.17482/uumfd.435723
fatcat:vq7fmf7vgfhafltj3liiyhvnlu
ACTL: Asymmetric Convolutional Transfer Learning for Tree Species Identification Based on Deep Neural Network
2021
IEEE Access
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Convolutional neural networks (CNN) have achieved great success in hyperspectral image (HSI) classification. ...
the classification of hyperspectral remote sensing images. ...
Li et al. proposed a method of hyperspectral image classification based on pixel pair features, and CNN was used to extract pixel pair models [24] from hyperspectral images and construct deep pixel pair ...
doi:10.1109/access.2021.3051015
fatcat:jqdkrp3gyvg4jlb7i5u6wwmf5a
Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios
[chapter]
2020
Advances in Computer Vision and Pattern Recognition
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2020 pre-print arXiv:2007.08592v1 fatcat:7lrxy7e52fhdhnqk5g44x25wru
Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery ...
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 ...
[66] introduced the pretrained model idea [1] for hyperspectral image classification. ...
doi:10.1007/978-3-030-38617-7_5
fatcat:23ibk4ojbvepbpikxgjxan4i6e
Trends in Deep Learning for Medical Hyperspectral Image Analysis
2021
IEEE Access
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with kernel fusion [51] Classification Blood cell classification based on hyperspectral imaging with modulated Gabor and CNN [52] Medical hyperspectral image classification based on end-to-end fusion ...
[35] Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model [55] Convolutional neural network for medical hyperspectral image classification ...
doi:10.1109/access.2021.3068392
fatcat:mxse6n6f7bbbrognlnbzponr7u
Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification
2020
IEEE Transactions on Geoscience and Remote Sensing
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Hence it would be conceptually of great interest to explore networks that are able to exploit labeled and unlabeled data simultaneously for hyperspectral image classification. ...
Unlike existing CNNs and RNNs that receive pixels or patches of a hyperspectral image as inputs, this network takes the whole image (including both labeled and unlabeled data) in. ...
set such as ImageNet in computer vision for hyperspectral image classification being difficult. ...
doi:10.1109/tgrs.2020.2973363
fatcat:2xt4zpifnbbzbhmpb5xcxpanoy
Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification
2019
IEEE Transactions on Geoscience and Remote Sensing
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We theoretically analyze and discuss why such a spectral attention module helps in a CNN for hyperspectral image classification. ...
Over the past few years, hyperspectral image classification using convolutional neural networks (CNNs) has progressed significantly. ...
attention on the spectral domain, for hyperspectral image classification. ...
doi:10.1109/tgrs.2019.2933609
fatcat:st3sc3fupvfuvpjwi7s62wkphq
Adversarial Learning based Discriminative Domain Adaptation for Geospatial Image Analysis
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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We test our framework for two very different applications of remote sensing imagery, multiclass classification in hyperspectral images and semantic segmentation in large scale satellite images. ...
For hyperspectral image analysis two datasets were used: the University of Houston shadow data was used for quantifying the efficacy of our approach to varying illumination, and the Botswana data was used ...
For hyperspectral image classification we use both spatial and spectral information with threedimensional convolution neural networks (3D-CNNs), which take advantage of sequential band information that ...
doi:10.1109/jstars.2021.3132259
fatcat:5ppi25cwirc2bmnlgolauiwga4
Detection of endogenous foreign bodies in Chinese hickory nuts by hyperspectral spectral imaging at the pixel level
2022
International Journal of Agricultural and Biological Engineering
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In this study, a deep learning approach based on a two-dimensional convolutional neural network (2D CNN) and long short-term memory (LSTM) integrated with hyperspectral imaging for distinguishing the shells ...
The results showed that the 2D CNN-LSTM model achieved the best performance with an overall classification accuracy of 99.0%. ...
Conclusions In this study, a deep learning approach was proposed for the detection of endogenous foreign bodies in Chinese hickory nuts based on hyperspectral spectral imaging and 2D CNN-LSTM. ...
doi:10.25165/j.ijabe.20221502.6881
fatcat:fm3ryh7skzderkg6k7h2pbehwi
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