- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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) - Adversarial Domain Adaptation with a Domain Similarity Discriminator for Semantic Segmentation of Urban Areas
摘要： Existing semantic segmentation models of urban areas have shown to perform well in a supervised setting. However, collecting lots of annotated images from each city to train such models is time-consuming or difficult. In addition, when transferring the segmentation model from the trained city (source domain) to an unseen city (target domain), the performance will largely degrade due to the domain shift. For this reason, we propose a domain adaptation method with a domain similarity discriminator to eliminate such domain shift in the framework of adversarial learning. Contrary to the single-input adversarial network, our domain similarity discriminator, which consists of a Siamese network, is able to measure the similarity of the pairwise-input data. In this way, we can use more information about the pairwise-input to measure the similarity between different distributions so as to address the problem of domain shift. Experimental results demonstrate that our approach outperforms the competing methods on three different cities.
关键词： domain adaptation,urban areas,semantic segmentation,domain shift,Siamese network
更新于2025-09-23 15:22:29
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Robust Hyperspectral Image Domain Adaptation With Noisy Labels
摘要： In hyperspectral image (HSI) classification, domain adaptation (DA) methods have been proved effective to address unsatisfactory classification results caused by the distribution difference between training (i.e., source domain) and testing (i.e., target domain) pixels. However, these methods rely on accurate labels in source domain, and seldom consider the performance drop resulted by noisy label, which often happens since labeling pixel in HSI is a challenging task. To improve the robustness of DA method to label noise, we propose a new unsupervised HSI DA method, which is constructed from both feature-level and classifier-level. First, a linear transformation function is learned in feature-level to align the source (domain) subspace with the target (domain) subspace. Then, a robust low-rank representation based classifier is developed to well cope with the features obtained from the aligned subspace. Since both subspace alignment and the classifier are immune to noisy labels, the proposed method obtains good classification results when confronting with noisy labels in source domain. Experimental results on two DA benchmarks demonstrate the effectiveness of the proposed method.
关键词： low-rank representation,hyperspectral image (HSI) classification,Domain adaptation,subspace alignment
更新于2025-09-23 15:22:29
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Discriminative Transfer Joint Matching for Domain Adaptation in Hyperspectral Image Classification
摘要： Domain adaptation, which aims at learning an accurate classifier for a new domain (target domain) using labeled information from an old domain (source domain), has shown promising value in remote sensing fields yet still been a challenging problem. In this letter, we focus on knowledge transfer between hyperspectral remotely sensed images in the context of land-cover classification under unsupervised setting where labeled samples are available only for the source image. Specifically, a discriminative transfer joint matching (DTJM) method is proposed, which matches source and target features in the kernel principal component analysis space by minimizing the empirical maximum mean discrepancy, performs instance reweighting by imposing an l2,1-norm on the embedding matrix, and preserves the local manifold structure of data from different domains and meanwhile maximizes the dependence between the embedding and labels. The proposed approach is compared with some state-of-the-art feature extraction techniques with and without using label information of source data. Experimental results on two benchmark hyperspectral data sets show the effectiveness of the proposed DTJM.
关键词： hyperspectral image classification,domain adaptation,Discriminative transfer joint matching (DTJM),manifold
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 (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Recent Advances and Opportunities in Scene Classification of Aerial Images with Deep Models
摘要： Scene classification is a fundamental task in interpretation of remote sensing images, and has become an active research topic in remote sensing community due to its important role in a wide range of applications. Over the past years, tremendous efforts have been made for developing powerful approaches for scene classification of remote sensing images, evolving from the traditional bag-of-visual-words model to the new generation deep convolutional neural networks (CNNs). The deep CNN based methods have exhibited remarkable break-through on performance, dramatically outperforming previous methods which strongly rely on hand-crafted features. However, performance with deep CNNs has gradually plateaued on existing public scene datasets, due to the notable drawbacks of these datasets, such as the small scale and low-diversity of training samples. Therefore, to promote the development of new methods and move the scene classification task a step further, we deeply discuss the existing problems in scene classification task, and accordingly present three open directions. We believe these potential directions will be instructive for the researchers in this field.
关键词： Scene classification,deep models,domain adaptation,datasets,scene caption
更新于2025-09-23 15:22:29
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Domain Adaptation With Discriminative Distribution and Manifold Embedding for Hyperspectral Image Classification
摘要： Hyperspectral remote sensing image classification has drawn a great attention in recent years due to the development of remote sensing technology. To build a high confident classifier, the large number of labeled data is very important, e.g., the success of deep learning technique. Indeed, the acquisition of labeled data is usually very expensive, especially for the remote sensing images, which usually needs to survey outside. To address this problem, in this letter, we propose a domain adaptation method by learning the manifold embedding and matching the discriminative distribution in source domain with neural networks for hyperspectral image classification. Specifically, we use the discriminative information of source image to train the classifier for the source and target images. To make the classifier can work well on both domains, we minimize the distribution shift between the two domains in an embedding space with prior class distribution in the source domain. Meanwhile, to avoid the distortion mapping of the target domain in the embedding space, we try to keep the manifold relation of the samples in the embedding space. Then, we learn the embedding on source domain and target domain by minimizing the three criteria simultaneously based on a neural network. The experimental results on two hyperspectral remote sensing images have shown that our proposed method can outperform several baseline methods.
关键词： neural network,hyperspectral image classification,maximum mean discrepancy (MMD),remote sensing,Domain adaptation,manifold embedding
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Defect Analysis in CSS and Sputtered CdSe <sub/>x</sub> Te <sub/>1-x</sub> Thin Films
摘要： This paper addresses a new person reidenti?cation problem without label information of persons under nonoverlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched (negative) and unlabeled image pairs from target domain cameras, we propose an adaptive ranking support vector machines (AdaRSVMs) method for reidenti?cation under target domain cameras without person labels. To overcome the problems introduced due to the absence of matched (positive) image pairs in the target domain, we relax the discriminative constraint to a necessary condition only relying on the positive mean in the target domain. To estimate the target positive mean, we make use of all the available data from source and target domains as well as constraints in person reidenti?cation. Inspired by adaptive learning methods, a new discriminative model with high con?dence in target positive mean and low con?dence in target negative image pairs is developed by re?ning the distance model learnt from the source domain. Experimental results show that the proposed AdaRSVM outperforms existing supervised or unsupervised, learning or non-learning reidenti?cation methods without using label information in target cameras. Moreover, our method achieves better reidenti?cation performance than existing domain adaptation methods derived under equal conditional probability assumption.
关键词： Person re-identi?cation,ranking SVMs,target positive mean,adaptive learning,domain adaptation
更新于2025-09-23 15:19:57
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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 Power & Energy Society General Meeting (PESGM) - Atlanta, GA, USA (2019.8.4-2019.8.8)] 2019 IEEE Power & Energy Society General Meeting (PESGM) - Fault Location in Ungrounded Photovoltaic System Using Wavelets and ANN
摘要： This paper addresses a new person reidenti?cation problem without label information of persons under nonoverlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched (negative) and unlabeled image pairs from target domain cameras, we propose an adaptive ranking support vector machines (AdaRSVMs) method for reidenti?cation under target domain cameras without person labels. To overcome the problems introduced due to the absence of matched (positive) image pairs in the target domain, we relax the discriminative constraint to a necessary condition only relying on the positive mean in the target domain. To estimate the target positive mean, we make use of all the available data from source and target domains as well as constraints in person reidenti?cation. Inspired by adaptive learning methods, a new discriminative model with high con?dence in target positive mean and low con?dence in target negative image pairs is developed by re?ning the distance model learnt from the source domain. Experimental results show that the proposed AdaRSVM outperforms existing supervised or unsupervised, learning or non-learning reidenti?cation methods without using label information in target cameras. Moreover, our method achieves better reidenti?cation performance than existing domain adaptation methods derived under equal conditional probability assumption.
关键词： adaptive learning,target positive mean,ranking SVMs,domain adaptation,Person re-identi?cation
更新于2025-09-23 15:19:57
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Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea
摘要： The applicability of deep learning to remote sensing is rapidly increasing in accordance with the improvement in spatiotemporal resolution of satellite images. However, unlike satellite images acquired in near-real-time over wide areas, there are limited amount of labeled data used for model training. In this article, three kinds of deep learning applications—data augmentation, semisupervised classification, and domain-adapted architecture—were tested in an effort to overcome the limitation of insufficient labeled data. Among the diverse tasks that can be used for classification, rice paddy detection in South Korea was performed for its ability to fully utilize the advantages of deep learning and high spatiotemporal image resolution. In the process of designing each application, the domain knowledge of remote sensing and rice phenology was integrated. Then, all possible combinations of the three applications were examined and evaluated with pixel-based comparisons in various environments and city-level comparisons using national statistics. The results of this article indicated that all combinations of the applications can contribute to increase classification performance, even though the uncertainty involved in imitating or utilizing unlabeled data remains. As the effectiveness of the proposed applications was experimentally confirmed, enhancement in the applicability of deep learning was expected in various remote sensing areas. In particular, the proposed applications would be significant when they are applied to a wide range of study areas and high-resolution images, as they tend to require a large amount of learning data from diverse environments, owing to high intraclass heterogeneity.
关键词： remote sensing,semisupervised classification,data labeling,deep learning,Data augmentation,domain adaptation
更新于2025-09-19 17:13:59
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[IEEE 2019 11th Electrical Engineering Faculty Conference (BulEF) - Varna, Bulgaria (2019.9.11-2019.9.14)] 2019 11th Electrical Engineering Faculty Conference (BulEF) - Operating regimes of a rooftop photovoltaic installation
摘要： This paper addresses a new person reidenti?cation problem without label information of persons under nonoverlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched (negative) and unlabeled image pairs from target domain cameras, we propose an adaptive ranking support vector machines (AdaRSVMs) method for reidenti?cation under target domain cameras without person labels. To overcome the problems introduced due to the absence of matched (positive) image pairs in the target domain, we relax the discriminative constraint to a necessary condition only relying on the positive mean in the target domain. To estimate the target positive mean, we make use of all the available data from source and target domains as well as constraints in person reidenti?cation. Inspired by adaptive learning methods, a new discriminative model with high con?dence in target positive mean and low con?dence in target negative image pairs is developed by re?ning the distance model learnt from the source domain. Experimental results show that the proposed AdaRSVM outperforms existing supervised or unsupervised, learning or non-learning reidenti?cation methods without using label information in target cameras. Moreover, our method achieves better reidenti?cation performance than existing domain adaptation methods derived under equal conditional probability assumption.
关键词： ranking SVMs,target positive mean,domain adaptation,Person re-identi?cation,adaptive learning
更新于2025-09-19 17:13:59