- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Superpixel-Based Semisupervised Active Learning for Hyperspectral Image Classification
摘要： In this work, we propose a new semisupervised active learning approach for hyperspectral image classification. The proposed method aims at improving machine generalization by using pseudolabeled samples, both confident and informative, which are automatically and actively selected, via semisupervised learning. The learning is performed under two assumptions: a local one for the labeling via a superpixel-based constraint dedicated to the spatial homogeneity and adaptivity into the pseudolabels, and a global one modeling the data density by a multinomial logistic regressor with a Markov random field regularizer. Furthermore, we propose a density-peak-based augmentation strategy for pseudolabels, due to the fact that the samples without manual labels in their superpixel neighborhoods are out of reach for the automatic sampling. Three real hyperspectral datasets were used in our experiments to evaluate the effectiveness of the proposed superpixel-based semisupervised learning approach. The obtained results indicate that the proposed approach can greatly improve the potential for semisupervised learning in hyperspectral image classification.
关键词： semisupervised learning,hyperspectral image classification,superpixel,clustering,Active learning
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Cyberworlds (CW) - Singapore, Singapore (2018.10.3-2018.10.5)] 2018 International Conference on Cyberworlds (CW) - Towards Automatic Optical Inspection of Soldering Defects
摘要： This paper proposes a method for automatic image-based classification of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Machine learning-based approaches are frequently used for image-based inspection. However, a main challenge is to manually create sufficiently large labeled training databases to allow for high accuracy of defect detection. Creating such large training databases is time-consuming, expensive, and often unfeasible in industrial production settings. In order to address this problem, an active learning framework is proposed which starts with only a small labeled subset of training data. The labeled dataset is then enlarged step-by-step by combining K-means clustering with active user input to provide representative samples for the training of an SVM classifier. Evaluations on two databases with insufficient and shifting solder joints samples have shown that the proposed method achieved high accuracy while requiring only minimal user input. The results also demonstrated that the proposed method outperforms random and representative sampling by ~ 3.2% and ~ 2.7%, respectively, and it outperforms the uncertainty sampling method by ~ 0.5%.
关键词： Classification of solder joint defects,active learning,Automatic Optical Inspection (AOI),SVM classifier,K-means
更新于2025-09-23 15:23:52
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[IEEE 2018 12th International Conference on Sensing Technology (ICST) - Limerick, Ireland (2018.12.4-2018.12.6)] 2018 12th International Conference on Sensing Technology (ICST) - EDA Sensor-based Evaluation of a Vegetation Succession Learning System
摘要： There are many forest problems that need to be solved to move toward a sustainable society; therefore, educating people, especially children, about forest problems is very important. Although schools conventionally educate children through textbooks, children are not expected to learn about forest problems actively from only textbooks. Therefore, this study develops game-type learning material that enables students learn complex mechanisms of vegetation succession and the actual state of succession, while taking interest in forest problems, by performing forest management themselves. For evaluation of the system, we measure the electrodermal activity (EDA) because using a conventional questionnaire can only qualitatively evaluate whether learning using the system is more interesting than when using text. EDA sensors are used to measure physiological response of participants reading texts and those using the system; the interest when using the system is evaluated quantitatively. The experiment shows that the change of EDA increases when using the system, which suggests that using the system is more interesting and can be better for learning than reading text.
关键词： Active Learning,SCR,Electrodermal Activity,Forest management game
更新于2025-09-19 17:15:36
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Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model for Hyperspectral Image Classification
摘要： Deep learning has recently attracted significant attention in the field of hyperspectral images (HSIs) classification. However, the construction of an efficient deep neural network mostly relies on a large number of labeled samples being available. To address this problem, this paper proposes a unified deep network, combined with active transfer learning (TL) that can be well-trained for HSIs classification using only minimally labeled training data. More specifically, deep joint spectral–spatial feature is first extracted through hierarchical stacked sparse autoencoder (SSAE) networks. Active TL is then exploited to transfer the pretrained SSAE network and the limited training samples from the source domain to the target domain, where the SSAE network is subsequently fine-tuned using the limited labeled samples selected from both source and target domains by the corresponding active learning (AL) strategies. The advantages of our proposed method are threefold: 1) the network can be effectively trained using only limited labeled samples with the help of novel AL strategies; 2) the network is flexible and scalable enough to function across various transfer situations, including cross data set and intraimage; and 3) the learned deep joint spectral–spatial feature representation is more generic and robust than many joint spectral–spatial feature representations. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based methods, on three popular data sets.
关键词： multiple-feature representation,transfer learning (TL),hyperspectral image (HSI) classification,deep learning,Active learning (AL),stacked sparse autoencoder (SSAE)
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 - Active Manifold Learning for Hyperspectral Image Classification
摘要： Hyperspectral image classification via supervised approaches is often affected by the high dimensionality of the spectral signatures and the relative scarcity of training samples. Dimensionality reduction (DR) and active learning (AL) are two techniques that have been investigated independently to address these two problems. Considering the nonlinear property of the hyperspectral data and the necessity of applying AL adaptively, in this paper, we propose to integrate manifold and active learning into a unique framework to alleviate the aforementioned two issues simultaneously. In particular, supervised Isomap is adopted for DR for the training set, followed by an out-of-sample extension approach to project the large amount of unlabeled samples into previously learned embedding space. Finally, AL is performed in conjunction with k-nearest neighbor (kNN) classification in the embedded feature space. Experiments on a benchmark hyperspectral dataset illustrate the effectiveness of the proposed framework in terms of DR and the feature space refinement.
关键词： classification,manifold learning,hyperspectral images,Active learning,out-of-sample extension
更新于2025-09-10 09:29:36
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[IEEE 2018 International Conference on Content-Based Multimedia Indexing (CBMI) - La Rochelle (2018.9.4-2018.9.6)] 2018 International Conference on Content-Based Multimedia Indexing (CBMI) - Active Learning to Assist Annotation of Aerial Images in Environmental Surveys
摘要： Nowadays, remote sensing technologies greatly ease environmental assessment using aerial images. Such data are most often analyzed by a manual operator, leading to costly and non scalable solutions. In the fields of both machine learning and image processing, many algorithms have been developed to fasten and automate this complex task. Their main common assumption is the need to have prior ground truth available. However, for field experts or engineers, manually labeling the objects requires a time-consuming and tedious process. Restating the labeling issue as a binary classification one, we propose a method to assist the costly annotation task by introducing an active learning process, considering a query-by-group strategy. Assuming that a comprehensive context may be required to assist the annotator with the labeling task of a single instance, the labels of all the instances of an image are indeed queried. A score based on instances distribution is defined to rank the images for annotation and an appropriate retraining step is derived to simultaneously reduce the interaction cost and improve the classifier performances at each iteration. A numerical study on real images is conducted to assess the algorithm performances. It highlights promising results regarding the classification rate along with the chosen re-training strategy and the number of interactions with the user.
关键词： human-in-the-loop,aerial images,object detection,Active learning,data annotation
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 - Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification
摘要： In this paper, we propose a wide contextual residual network (WCRN) with active learning (AL) for remote sensing image (RSI) classification. Although ResNets have achieved great success in various applications (e.g. RSI classification), its performance is limited by the requirement of abundant labeled samples. As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples. Specifically, we first design a wide contextual residual network for RSI classification. We then integrate it with AL to achieve good machine generalization with limited number of training sampling. Experimental results on the University of Pavia and Flevoland datasets demonstrate that the proposed WCRN with AL can significantly reduce the needs of samples.
关键词： SAR,Residual networks,classification,hyperspectral image,remote sensing,active learning
更新于2025-09-10 09:29:36
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Fast Active Learning for Hyperspectral Image Classification using Extreme Learning Machine
摘要： Due to undulating and complexity of the earth’s surface, obtaining the training samples for remote sensing data is time consuming and expensive. Therefore, it is highly desirable to design a model that uses as few labelled samples as possible and reducing the computational time. Several active learning (AL) algorithms have been proposed in the literature for the classification of hyperspectral images (HSI).However, its performance in term of computational time has not been focused yet. In this paper, we have proposed AL approach based on Extreme Learning Machine (ELM) that effectively decreases the computational time while maintaining the classification accuracy. Further, the effectiveness of the proposed approach has been depicted by comparing its performance with state-of-the-art AL algorithms in terms of classification accuracy and computational time as well. The ELM based active learning (ELM-AL) with different query strategies were conducted on two HSI data sets. The proposed approach achieves the classification accuracy up to 90% which is comparable to support vector machine (SVM) based AL (SVM-AL) approach but effectively reduces the computational time significantly by 1000 times. Thus proposed system shows the encouraging results with adequate classification accuracy while reducing the computation time drastically.
关键词： Uncertainty sampling,Remote Sensing Image,Extreme learning machine,Classification,Active learning,Uncertainty measure,Hyperspectral Image
更新于2025-09-09 09:28:46