The Experts below are selected from a list of 200625 Experts worldwide ranked by ideXlab platform
Liangpei Zhang - One of the best experts on this subject based on the ideXlab platform.
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Kernel Slow Feature Analysis for Scene Change Detection
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Liangpei ZhangAbstract:Scene Change Detection between multitemporal image scenes can be used to interpret the variation of regional land use, and has significant potential in the application of urban development monitoring at the semantic level. The traditional methods directly comparing the independent semantic classes neglect the temporal correlation, and thus suffer from accumulated classification errors. In this paper, we propose a novel scene Change Detection method via kernel slow feature analysis (KSFA) and postclassification fusion, which integrates independent scene classification with scene Change Detection to accurately determine scene Changes and identify the “from-to” transition type. After representation with the bag-of-visual-words model, KSFA is proposed to extract the nonlinear temporally invariant features, to better measure the Change probability between corresponding multitemporal image scenes. Two postclassification fusion methods, which are based on Bayesian theory and predefined rules, respectively, are then employed to identify the optimal coupled class combinations of multitemporal scene pairs. Furthermore, in addition to identifying semantic Changes, the proposed method can also improve the performance of scene classification, since the unChanged scenes are more likely to belong to the same class. Two experiments with high-resolution remote sensing image scene data sets confirm that the proposed method can increase the accuracy of scene Change Detection, scene transition identification, and scene classification.
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a scene Change Detection framework for multi temporal very high resolution remote sensing images
Signal Processing, 2016Co-Authors: Lefei Zhang, Liangpei ZhangAbstract:The technology of computer vision and image processing is attracting more and more attentions in recent years, and has been applied in many research areas like remote sensing image analysis. Change Detection with multi-temporal remote sensing images is very important for the dynamic analysis of landscape variations. The abundant spatial information offered by very high resolution (VHR) images makes it possible to identify the semantic classes of image scenes. Compared with the traditional approaches, scene Change Detection can provide a new point of view for the semantic interpretation of land-use transitions. In this paper, for the first time, we explore a scene Change Detection framework for VHR images, with a bag-of-visual-words (BOVW) model and classification-based methods. Image scenes are represented by a word frequency with three kinds of multi-temporal learned dictionary, i.e., the separate dictionary, the stacked dictionary, and the union dictionary. Three features (multispectral raw pixel; mean and standard deviation; and SIFT) and their combinations were tested in scene Change Detection. Post-classification and compound classification were evaluated for their performances in the "from-to" Change results. Two multi-temporal scene datasets were used to quantitatively evaluate the proposed scene Change Detection approach. The results indicate that the proposed scene Change Detection framework can obtain a satisfactory accuracy and can effectively analyze land-use Changes, from a semantic point of view. A scene Change Detection framework for multi-temporal RS imagery is explored.Three different features and their combinations are tested.Three types of dictionary learning with temporal information are evaluated.It can analyze city development with semantic interpretation of land-use Change.
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slow feature analysis for Change Detection in multispectral imagery
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Liangpei ZhangAbstract:Change Detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the Change Detection problem is to exploit all the available spectral bands to detect the spectral Changes. However, in practice, the temporal spectral variance makes it difficult to separate Changes and nonChanges. In this paper, we propose a novel slow feature analysis (SFA) algorithm for Change Detection. Compared with Changed pixels, the unChanged ones should be spectrally invariant and varying slowly across the multitemporal images. SFA extracts the most temporally invariant component from the multitemporal images to transform the data into a new feature space. In this feature space, the differences in the unChanged pixels are suppressed so that the Changed pixels can be better separated. Three SFA Change Detection approaches, comprising unsupervised SFA, supervised SFA, and iterative SFA, are constructed. Experiments on two groups of real Enhanced Thematic Mapper data sets show that our proposed method performs better in detecting Changes than the other state-of-the-art Change Detection methods.
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fault tolerant building Change Detection from urban high resolution remote sensing imagery
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Yuqi Tang, Xin Huang, Liangpei ZhangAbstract:This letter proposes a novel Change Detection model, focusing on building Change information extraction from urban high-resolution imagery. It consists of two blocks: 1) building interest-point Detection, using the morphological building index (MBI) and the Harris detector; and 2) multitemporal building interest-point matching and the fault-tolerant Change Detection. The proposed method is insensitive to the geometrical differences of buildings caused by different imaging conditions in the multitemporal high-resolution imagery and is able to significantly reduce false alarms. Experiments showed that the proposed method was effective for building Change Detection from multitemporal urban high-resolution images. Moreover, the effectiveness of the algorithm was validated by comparing with the morphological Change vector analysis (CVA), parcel-based CVA, and MBI-based CVA.
Jakub Sido - One of the best experts on this subject based on the ideXlab platform.
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uwb at semeval 2020 task 1 lexical semantic Change Detection
International Conference on Computational Linguistics, 2020Co-Authors: Ondrej Prazak, Pavel Priban, Stephen Taylor, Jakub SidoAbstract:In this paper, we describe our method for Detection of lexical semantic Change, i.e., word sense Changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary Change Detection, and 4th in Sub-task 2: ranked Change Detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.
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uwb at semeval 2020 task 1 lexical semantic Change Detection
arXiv: Computation and Language, 2020Co-Authors: Ondrej Prazak, Stephen Taylor, Pavel Přibaň, Jakub SidoAbstract:In this paper, we describe our method for the Detection of lexical semantic Change, i.e., word sense Changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked $1^{st}$ in Sub-task 1: binary Change Detection, and $4^{th}$ in Sub-task 2: ranked Change Detection. Our method is fully unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and Orthogonal Transformation; and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.
Weimin Wang - One of the best experts on this subject based on the ideXlab platform.
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weakly supervised silhouette based semantic scene Change Detection
International Conference on Robotics and Automation, 2020Co-Authors: Ken Sakurada, Mikiya Shibuya, Weimin WangAbstract:This paper presents a novel semantic scene Change Detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic Change Detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into Change Detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example, images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the Change Detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we create a publicly available dataset for semantic Change Detection to evaluate the proposed method. The experimental results verified both the robustness to viewpoint difference in Change Detection task and the effectiveness for semantic Change Detection of the proposed networks. Our code and dataset are available at https://github.com/xdspacelab/sscdnet.
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weakly supervised silhouette based semantic scene Change Detection
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Ken Sakurada, Mikiya Shibuya, Weimin WangAbstract:This paper presents a novel semantic scene Change Detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic Change Detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into Change Detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example, images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the Change Detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we create a publicly available dataset for semantic Change Detection to evaluate the proposed method. The experimental results verified both the robustness to viewpoint difference in Change Detection task and the effectiveness for semantic Change Detection of the proposed networks. Our code and dataset are available at this https URL.
Ondrej Prazak - One of the best experts on this subject based on the ideXlab platform.
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uwb at semeval 2020 task 1 lexical semantic Change Detection
International Conference on Computational Linguistics, 2020Co-Authors: Ondrej Prazak, Pavel Priban, Stephen Taylor, Jakub SidoAbstract:In this paper, we describe our method for Detection of lexical semantic Change, i.e., word sense Changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary Change Detection, and 4th in Sub-task 2: ranked Change Detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.
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uwb at semeval 2020 task 1 lexical semantic Change Detection
arXiv: Computation and Language, 2020Co-Authors: Ondrej Prazak, Stephen Taylor, Pavel Přibaň, Jakub SidoAbstract:In this paper, we describe our method for the Detection of lexical semantic Change, i.e., word sense Changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked $1^{st}$ in Sub-task 1: binary Change Detection, and $4^{th}$ in Sub-task 2: ranked Change Detection. Our method is fully unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and Orthogonal Transformation; and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.
Ken Sakurada - One of the best experts on this subject based on the ideXlab platform.
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weakly supervised silhouette based semantic scene Change Detection
International Conference on Robotics and Automation, 2020Co-Authors: Ken Sakurada, Mikiya Shibuya, Weimin WangAbstract:This paper presents a novel semantic scene Change Detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic Change Detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into Change Detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example, images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the Change Detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we create a publicly available dataset for semantic Change Detection to evaluate the proposed method. The experimental results verified both the robustness to viewpoint difference in Change Detection task and the effectiveness for semantic Change Detection of the proposed networks. Our code and dataset are available at https://github.com/xdspacelab/sscdnet.
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weakly supervised silhouette based semantic scene Change Detection
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Ken Sakurada, Mikiya Shibuya, Weimin WangAbstract:This paper presents a novel semantic scene Change Detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic Change Detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into Change Detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example, images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the Change Detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we create a publicly available dataset for semantic Change Detection to evaluate the proposed method. The experimental results verified both the robustness to viewpoint difference in Change Detection task and the effectiveness for semantic Change Detection of the proposed networks. Our code and dataset are available at this https URL.