- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A review on graph-based semi-supervised learning methods for hyperspectral image classification
摘要： In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial information based graph semi-supervised classification. In addition, related techniques are categorized into the following sub-types: (1) Manifold representation based Graph Semi-supervised Learning for HSI Classification (2) Sparse representation based Graph Semi-supervised Learning for HSI Classification. For each technique, methodologies, training and testing samples, various technical difficulties, as well as performances, are discussed. Additionally, future research challenges imposed by the graph-based model are indicated.
关键词： Image classification,Hyperspectral images,Semi-supervised learning,Graph-based learning
更新于2025-09-23 15:23:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Learning Illuminant Estimation from Object Recognition
摘要： In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions.
关键词： Illuminant estimation,deep learning,convolutional neural networks,computational color constancy,semi-supervised learning
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fully Convolutional Semi-Supervised Gan for Polsar Classification
摘要： We propose a novel semi-supervised fully convolutional network for Polarimetric synthetic aperture radar (PolSAR) terrain classification. First, by designing a fully convolutional structure, we can perform pixel-based classification tasks. Then, by applying semi-supervised generative adversarial networks (GANs), we utilize both labeled and unlabeled samples and aim to obtain higher classification accuracy. Through a mini-max two-player game, GAN has better performance than other “single-player” classifiers. Finally, we combine the fully convolutional structure with the semi-supervised GAN. Our fully convolutional semi-supervised GAN (FC-SGAN) has excellent spatial feature learning ability and can perform end-to-end pixel-based classification tasks. Experimental results show that compared with existing works, the proposed method has better performances. Even when the training set gets smaller, our method keeps high accuracy.
关键词： terrain classification,fully convolutional network,generative adversarial network,semi-supervised learning
更新于2025-09-23 15:21:21
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[IEEE 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Chengdu, China (2018.7.15-2018.7.18)] 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification
摘要： Collecting label samples is quite costly and time consuming for hyperspectral image (HSI) classification tasks. Semi-supervised learning framework, which combines the intrinsic information of labeled and unlabeled samples can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this paper, we propose a novel framework for semi-supervised learning on multiple spectral-spatial graphs that is based on graph convolutional networks (SGCN). In the filtering operation on graphs we consider the spatial information and spectral signatures of HSI simultaneously. The experimental results on three real-life HSI data sets, i.e. Botswana Hyperion, Kennedy Space Center, and Indian Pines, show that the proposed SGCN can significantly improve the classification accuracy. For instance, the over accuracy on Indian Pine data is increased from 78% to 93%.
关键词： Hyperspectral image classification,Graph fourier transform,Graph convolutional,Neural networks,Semi-supervised learning
更新于2025-09-23 15:21:01
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Semi-supervised convolutional neural networks with label propagation for image classification
摘要： Over the past several years, deep learning has achieved promising performance in many visual tasks, e.g., face verification and object classification. However, a limited number of labeled training samples existing in practical applications is still a huge bottleneck for achieving a satisfactory performance. In this paper, we integrate class estimation of unlabeled training data with deep learning model which generates a novel semi-supervised convolutional neural network (SSCNN) trained by both the labeled training data and unlabeled data. In the framework of SSCNN, the deep convolution feature extraction and the class estimation of the unlabeled data are jointly learned. Specifically, deep convolution features are learned from the labeled training data and unlabeled data with confident class estimation. After the deep features are obtained, the label propagation algorithm is utilized to estimate the identities of unlabeled training samples. The alternative optimization of SSCNN makes the class estimation of unlabeled data more and more accurate due to the learned CNN feature more and more discriminative. We compared the proposed SSCNN with some representative semi-supervised learning approaches on MINIST and Cifar-10 databases. Extensive experiments on landmark databases show the effectiveness of our semi-supervised deep learning framework.
关键词： convolutional neural network,semi-supervised learning,label propagation
更新于2025-09-23 15:21:01
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[IEEE 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) - Bangalore, India (2018.5.18-2018.5.19)] 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) - Solar Photovoltaic Powered Smart Garbage Monitoring System Using GSM/GPS
摘要： In real-life problems, the following semi-supervised domain adaptation scenario is often encountered: we have full access to some source data, which is usually very large; the target data distribution is under certain unknown transformation of the source data distribution; meanwhile, only a small fraction of the target instances come with labels. The goal is to learn a prediction model by incorporating information from the source domain that is able to generalize well on the target test instances. We consider an explicit form of transformation functions and especially linear transformations that maps examples from the source to the target domain, and we argue that by proper preprocessing of the data from both source and target domains, the feasible transformation functions can be characterized by a set of rotation matrices. This naturally leads to an optimization formulation under the special orthogonal group constraints. We present an iterative coordinate descent solver that is able to jointly learn the transformation as well as the model parameters, while the geodesic update ensures the manifold constraints are always satis?ed. Our framework is suf?ciently general to work with a variety of loss functions and prediction problems. Empirical evaluations on synthetic and real-world experiments demonstrate the competitive performance of our method with respect to the state-of-the-art.
关键词： transfer learning,semi-supervised learning,Domain adaptation
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE International Conference on Space Optical Systems and Applications (ICSOS) - Portland, OR, USA (2019.10.14-2019.10.16)] 2019 IEEE International Conference on Space Optical Systems and Applications (ICSOS) - Inter-Satellite Integrated Laser Communication/Ranging Link with Feedback-Homodyne Detection and Fractional Symbol Ranging
摘要： In real-life problems, the following semi-supervised domain adaptation scenario is often encountered: we have full access to some source data, which is usually very large; the target data distribution is under certain unknown transformation of the source data distribution; meanwhile, only a small fraction of the target instances come with labels. The goal is to learn a prediction model by incorporating information from the source domain that is able to generalize well on the target test instances. We consider an explicit form of transformation functions and especially linear transformations that maps examples from the source to the target domain, and we argue that by proper preprocessing of the data from both source and target domains, the feasible transformation functions can be characterized by a set of rotation matrices. This naturally leads to an optimization formulation under the special orthogonal group constraints. We present an iterative coordinate descent solver that is able to jointly learn the transformation as well as the model parameters, while the geodesic update ensures the manifold constraints are always satis?ed. Our framework is suf?ciently general to work with a variety of loss functions and prediction problems. Empirical evaluations on synthetic and real-world experiments demonstrate the competitive performance of our method with respect to the state-of-the-art.
关键词： transfer learning,semi-supervised learning,Domain adaptation
更新于2025-09-19 17:13:59
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Two-Branch Network with Semi-Supervised Learning for Hyperspectral Classification
摘要： In order to promote progress on fusion and analysis methodologies for multi-source remote sensing data, The Image Analysis and Data Fusion Technical Committee organized the 2018 IEEE GRSS Data Fusion contest. In this contest, we proposed a two-branch convolution network for hyperspectral image classification with a data re-sampling strategy and semi-supervised learning to address three existing problems, i.e. multi-scale feature learning, data imbalance, and small size of the dataset. The contest showed that our proposal achieved the best performance on two metrics: the overall accuracy of 77.39% and a kappa coefficient of 0.76 on the hyperspectral images provided by 2018 IEEE GRSS Data Fusion Contest.
关键词： deep learning,Hyperspectral image,image classification,semi-supervised learning
更新于2025-09-10 09:29:36
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Semi-Supervised hyperspectral image classification using local low-rank representation
摘要： In the area of hyperspectral image (HSI) classification, graph-based semi-supervised learning (SSL) has been proved to be highly effective. Constructing a proper graph is critical for graph-based SSL tasks. In HSI, spectral distance is widely used to calculate the weight of graph edge, though it can be influenced by noise and outliers. Meanwhile, links among all the data points are incorporated in the graph, including those from different subspaces. Thus the constructed graph might contain incorrect information. In this letter, a novel semi-supervised HSI classification method using local low-rank representation (SL2R) is proposed. Edge weight calculation will not be affected by noise or outliers thanks to the robustness of low-rank representation (LRR). Since each graph is constructed at local level, where pixels are basically embedded in the same subspace, links among uncorrelated pixels can be removed. Moreover, spatial context is naturally characterized by low-rank constraint on adjacent pixels. Experimental results on two data sets (Indian Pines and Botswana) confirm the effectiveness of the proposed method.
关键词： spectral-spatial classification,semi-supervised learning,hyperspectral image classification,low-rank representation
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Semi-Supervised Object Detection in Remote Sensing Images Using Generative Adversarial Networks
摘要： Object detection is a challenging task in computer vision. Now many detection networks can get a good detection result when applying large training dataset. However, annotating sufficient amount of data for training is often time-consuming. To address this problem, a semi-supervised learning based method is proposed in this paper. Semi-supervised learning trains detection networks with few annotated data and massive amount of unannotated data. In the proposed method, Generative Adversarial Network is applied to extract data distribution from unannotated data. The extracted information is then applied to improve the performance of detection network. Experiment shows that the method in this paper greatly improves the detection performance compared w1ith supervised learning using only few annotated data. The results prove that it is possible to achieve acceptable detection result when only few target object is annotated in the training dataset.
关键词： generative adversarial networks (GAN),convolutional neural networks (CNN),Semi-supervised learning,object detection
更新于2025-09-10 09:29:36