- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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 - Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
摘要： Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat-8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adapted to resemble Proba-V characteristics and tested on a large set of real Proba-V scenes. Developed models outperform current operational Proba-V cloud detection without being trained with any real Proba-V data. Moreover, cloud detection accuracy can be further increased if the CNN are fine-tuned using a limited amount of Proba-V data.
关键词： Proba-V,Transfer Learning,CNN,Cloud detection,Domain Adaptation,Landsat-8
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 Remote Sensing Classification Via Associative Transfer
摘要： Images classification is an essential field in remote sensing community. However, a variety of target shapes, as well as changing conditions during multiple time periods and different areas usually result in shifts in classification. This problem can affect classification results seriously. Although the affection is significant in remote sensing classification, very few people have considered this issue, and have solved it. In this paper, we introduce the associative domain adaptation (ADA) method to address this challenge. We apply this algorithm to two public remote sensing datasets. One is famous UC Merced dataset; another is NWPU-RESISC45 dataset which has a much more variance within the class. We then build a classification model by using UC Merced training images and labels as well as using training images from NWPU-RESISC45. This semi-supervised classification performance achieves an impressive test accuracy on the NWPU-RESISC45 test dataset.
关键词： Associative Domain Adaptation,Semi-Supervised Classification,Remote Sensing
更新于2025-09-10 09:29:36
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Centroid and Covariance Alignment-Based Domain Adaptation for Unsupervised Classification of Remote Sensing Images
摘要： A new domain adaptation algorithm based on the class centroid and covariance alignment (CCCA) is proposed for classification of remote sensing images. This approach exploits both the first- and second-order statistics to describe the data distribution and aligns the data distribution between domains on a per-class basis. Since the predicted labels of target data are used to estimate the two statistics, we applied overall centroid alignment (OCA) as a coarse domain adaptation strategy to improve the estimation accuracy. In addition, the OCA coarse adaptation in conjunction with CCCA refined adaptation can also benefit by incorporation of spatial information, resulting in a Spa_OCA_CCCA approach. The proposed approach is easy to implement, and only one parameter is required in the spatial filtering step. It does not require labeled information in the target domain and can achieve labor-free classification. The experimental results using Hyperion, National Center for Airborne Laser Mapping, and Worldview-2 remote sensing images demonstrated the effectiveness of the proposed approach.
关键词： Alignment,remote sensing,domain adaptation,classification
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 - Generative Adversarial Networks for Cross-Scene Classification in Remote Sensing Images
摘要： In this paper, we present a novel method for cross-scene classification in remote sensing images based on generative adversarial networks (GANs). To this end, we train in an adversarial manner an encoder-decoder network coupled with a discriminator network on labeled and unlabeled data coming from two different domains. The encoder-decoder network aims to reduce the discrepancy between the distributions of the two domains, while the discriminator tries to discriminate between them. At the end of the optimization process, we train an extra network on the obtained encoded labeled data and then classify the encoded unlabeled data. Experimental results on two datasets acquired over the cities of Potsdam and Vaihingen with spatial resolutions of 5cm and 9cm, respectively, confirm the promising capability of the proposed method.
关键词： domain adaptation,generative adversarial networks (GANs),Cross-scene classification
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 - Gan-Based Domain Adaptation for Object Classification
摘要： Recent trends in image classification focus on training deep neural networks that require having a large amount of training images related to the considered task. However, obtaining enough labeled image samples is often time-consuming and expensive. An alternative solution proposed is to transfer the knowledge learned while solving one problem to another but related problem, also called transfer learning. Domain adaptation is a type of transfer learning that deals with learning a model that performs well on two datasets that have different (but somehow correlated) data distributions. In this work, we present a new domain adaptation method based on generative adversarial networks (GANs) in the context of aerial image classification. Experimental results obtained on two datasets for a single object scenario show that the proposed method is particularly promising.
关键词： Deep learning,GAN,domain adaptation,transfer learning
更新于2025-09-09 09:28:46
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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 - Deep Domain Adaptation for Single-Shot Vehicle Detector in Satellite Images
摘要： In this paper, we designed unsupervised domain adaptation (DA) methods to vehicle detection in high-resolution satellite images. We applied two Single Shot MultiBox Detectors, which have advantages in handling image feature differences among various kinds of image data: Correlation Alignment DA (CORAL DA) and adversarial DA. These novel methods can much improve accuracy without annotated data by finding the common feature space of source and target domains and aligning the features. While a mean of average precision (AP) and F1 dropped from 84.1% in the source domain to 66.3% in the target domain, the CORAL DA and adversarial DA improved it to 76.8% and 75.9% respectively. These improvements were over a half of the performance degradation, indicating the usability of our methods.
关键词： CORAL,domain adaptation,vehicle detection,satellite images,single shot multibox detector (SSD),adversarial training
更新于2025-09-09 09:28:46