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Is Pretraining Necessary for Hyperspectral Image Classification? [article]

Hyungtae Lee, Sungmin Eum, Heesung Kwon
<span title="2019-01-24">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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?  ...  and ii) is the pre-training effective in furthering the performance?  ...  Is pre-trained network necessary for hyperspectral image classification? 2. Does a larger source dataset improve accuracy? 3.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.08658v1">arXiv:1901.08658v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2wequy42kbfqvajd4zeairiuia">fatcat:2wequy42kbfqvajd4zeairiuia</a> </span>
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Hyperspectral Image Classification [chapter]

Rajesh Gogineni, Ashvini Chaturvedi
<span title="2019-12-13">2019</span> <i title="IntechOpen"> Processing and Analysis of Hyperspectral Data [Working Title] </i> &nbsp;
Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images.  ...  Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel.  ...  Based on the usage of training sample, image classification task is categorized as supervised, unsupervised and semi-supervised hyperspectral image classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/intechopen.88925">doi:10.5772/intechopen.88925</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7ixv44bobbd3vkp7hn5c6tlb2y">fatcat:7ixv44bobbd3vkp7hn5c6tlb2y</a> </span>
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Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification

Hyungtae Lee, Sungmin Eum, Heesung Kwon
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4odsbtjobjalfki6xxabjpdu6y" style="color: black;">IEEE Transactions on Geoscience and Remote Sensing</a> </i> &nbsp;
A pretrain-finetune strategy is widely used to reduce the overfitting that can occur when data is insufficient for CNN training.  ...  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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tgrs.2022.3165441">doi:10.1109/tgrs.2022.3165441</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ndeh7kasbrgs7pqmpv4fy2anse">fatcat:ndeh7kasbrgs7pqmpv4fy2anse</a> </span>
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STRATEGIC OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL LAND COVER CLASSIFICATION

C. Buehler, F. Schenkel, W. Gross, G. Schaab, W. Middelmann
<span title="2020-08-21">2020</span> <i title="Copernicus GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/i74shj7anreaxjo327fokng66m" style="color: black;">The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</a> </i> &nbsp;
In this study, a model for the spectral classification of hyperspectral data is derived by strategically optimizing a convolutional neural network (1D-CNN).  ...  Hence, the ability of performing a meaningful classification only relying on spectral information is important.  ...  Figure 1 shows two sample image scenes. From band 12 to 25, the Cubert data is noisy for an unknown reason. This is considered later when evaluating the classification results.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/isprs-archives-xliii-b3-2020-363-2020">doi:10.5194/isprs-archives-xliii-b3-2020-363-2020</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/owj3konirva6bkfaqp5kgnoyfy">fatcat:owj3konirva6bkfaqp5kgnoyfy</a> </span>
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Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning

Haokui Zhang, Ying Li, Yenan Jiang, Peng Wang, Qiang Shen, Chunhua Shen
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4odsbtjobjalfki6xxabjpdu6y" style="color: black;">IEEE Transactions on Geoscience and Remote Sensing</a> </i> &nbsp;
Recently, hyperspectral image (HSI) classification approaches based on deep learning (DL) models have been proposed and shown promising performance.  ...  In this paper, we propose an end-to-end 3-D lightweight convolutional neural network (CNN) (abbreviated as 3-D-LWNet) for limited samples-based HSI classification.  ...  Unlike natural image classification, HSI classification is a classification task involving 3D data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tgrs.2019.2902568">doi:10.1109/tgrs.2019.2902568</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pwk7jd3fsra3xaufvattqucqpe">fatcat:pwk7jd3fsra3xaufvattqucqpe</a> </span>
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Deep learning for remote sensing image classification: A survey

Ying Li, Haokui Zhang, Xizhe Xue, Yenan Jiang, Qiang Shen
<span title="2018-05-17">2018</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/agmngghlr5hyrpto64zks3fhry" style="color: black;">Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery</a> </i> &nbsp;
Figure 4 shows a typical DBN for deep feature learning from hyperspectral images. In DBN, the output of the preceding RBM is used as input data for the next RBM.  ...  Yet, in the RS image domain, high-fidelity images with labels are rather limited. It is therefore, necessary to learn the features with unlabeled images.  ...  CONFLICT OF INTEREST The authors have declared no conflicts of interest for this article.  ... 
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Building instance classification using street view images

Jian Kang, Marco Körner, Yuanyuan Wang, Hannes Taubenböck, Xiao Xiang Zhu
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sgj4ockzhjenrlnkl2wsqjql64" style="color: black;">ISPRS journal of photogrammetry and remote sensing (Print)</a> </i> &nbsp;
Such classification is usually a patch-wise or pixel-wise labeling over the whole image.  ...  But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative.  ...  The authors would like to thank the reviewers for their valuable suggestions.  ... 
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Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation

Jingzhou Chen, Siyu Chen, Peilin Zhou, Yuntao Qian
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
for hyperspectral image classification.  ...  However, how to construct an efficient and powerful network suitable for hyperspectral data is still under exploration.  ...  The augmentation of training samples for pixel classification in hyperspectral images is not easy, and the main end of our paper is to show the effectiveness and power of the proposed model in the case  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2923776">doi:10.1109/access.2019.2923776</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6hkqdvxfijda5bzo32owdm4eaq">fatcat:6hkqdvxfijda5bzo32owdm4eaq</a> </span>
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Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images

Qingsong Xu, Xin Yuan, Chaojun Ouyang, Yue Zeng
<span title="2020-10-24">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
Second, an end-to-end spatial-spectralFFPNet is presented for classifying hyperspectral images.  ...  image classification.  ...  [40] was utilized as the baseline for hyperspectral image classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs12213501">doi:10.3390/rs12213501</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mgkchmbiqze57ckdefoc45o4ae">fatcat:mgkchmbiqze57ckdefoc45o4ae</a> </span>
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Spectral-Spatial Classification of Hyperspectral Images Using Label Dependence

Zhuangzhuang He, Hao Wu, Guodong Wu
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Deep learning, hyperspectral image, image classification, pattern recognition.  ...  Especially, the experimental results demonstrate that the joint spectral-spatial approach is effective in improving the accuracy of hyperspectral image classification.  ...  The authors would also like to thank Lijing Tu for her teaching and support, and the lab members for their company and encouragement (Fang, Ji, Su, Wu, and so on).  ... 
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Remote Sensing Image Scene Classification Using CNN-CapsNet

Wei Zhang, Ping Tang, Lijun Zhao
<span title="2019-02-28">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
Remote sensing image scene classification is one of the most challenging problems in understanding high-resolution remote sensing images.  ...  In detail, a pretrained deep CNN model that was fully trained on the ImageNet dataset is selected as a feature extractor in this paper.  ...  , sound event detection, object segmentation, and hyperspectral image classification.  ... 
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A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification

Qiqi Zhu, Weihuan Deng, Zhuo Zheng, Yanfei Zhong, Qingfeng Guan, Weihua Lin, Liangpei Zhang, Deren Li
<span title="2021-05-25">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/snhjqrgxbff5teva5lfasxmfr4" style="color: black;">IEEE Transactions on Cybernetics</a> </i> &nbsp;
Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success.  ...  Deep learning methods for HSI classification usually follow a patchwise learning framework.  ...  Therefore, it is necessary to introduce a novel sampling strategy and a suitable loss function to solve the problem of class long-tail distribution in hyperspectral image datasets.  ... 
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Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation [article]

Jingzhou Chen, Siyu Chen, Peilin Zhou, Yuntao Qian
<span title="2019-04-16">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
for hyperspectral image classification.  ...  However, how to construct an efficient and powerful network suitable for hyperspectral data is still under exploration.  ...  Because of rich spectral information of the hyperspectral data, pretraining the subnetwork used for spectral feature extracting with labeled samples not only reduces the feature dimensionality but also  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.07461v1">arXiv:1904.07461v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s2thgfwcobgdhefv5v67re5b4a">fatcat:s2thgfwcobgdhefv5v67re5b4a</a> </span>
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An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

Riyaaz Uddien Shaik, Giovanni Laneve, Lorenzo Fusilli
<span title="2022-03-04">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types.  ...  This paper presents an automatic semisupervised machine learning approach for discriminating between wildfire fuel types and a procedure for fuel mapping using hyperspectral imagery (HSI) from PRISMA,  ...  Acknowledgments: The authors would like to thank the Regional Administration of Sardinia and Italian Space Agency for the funding.  ... 
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Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

Jian Wang, Bizhi Wu, Markus V. Kohnen, Daqi Lin, Changcai Yang, Xiaowei Wang, Ailing Qiang, Wei Liu, Jianbin Kang, Hua Li, Jing Shen, Tianhao Yao (+3 others)
<span title="2021-03-30">2021</span> <i title="American Association for the Advancement of Science (AAAS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/45zzn4bvl5cd5ptvizrfz6emi4" style="color: black;">Plant Phenomics</a> </i> &nbsp;
Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed  ...  High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding.  ...  For breeders, the classification of new rice cultivars or hybrids is of particular Figure 6 : Evaluation of the hyperspectral measurements within and between lines.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.34133/2021/9765952">doi:10.34133/2021/9765952</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33851136">pmid:33851136</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8028843/">pmcid:PMC8028843</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fb2alxovr5grtm2nt2sjczdiqy">fatcat:fb2alxovr5grtm2nt2sjczdiqy</a> </span>
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