Proceedings Article10.1109/CEC.2014.6900269
A multiobjective optimization method based on MOEA/D and fuzzy clustering for change detection in SAR images
Wang Qiao,Hao Li,Maoguo Gong,Linzhi Su,Licheng Jiao +4 more
- 06 Jul 2014
- pp 3024-3029
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TL;DR: An innovation for change detection in synthetic aperture radar images is put forward that integrates evolutionary computation into fuzzy clustering process, and considers detail preserving capability and noise removing capability as two separate objectives for multiobjective optimization, and thus transforming the change detection problem into a multiobjectives optimization problem (MOP).
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Abstract: For the presence of speckle noise in SAR images, many change detection methods have been developed to suppress the effect of noise. However, all these methods will result in the loss of image details, and the trade-off between detail preserving and noise removing capability has become an urgent problem remaining to be settled. In this paper, we put forward an innovation for change detection in synthetic aperture radar images. It integrates evolutionary computation into fuzzy clustering process, and considers detail preserving capability and noise removing capability as two separate objectives for multiobjective optimization, and thus transforming the change detection problem into a multiobjective optimization problem (MOP). Experiments conducted on real S AR images confirm that the new approach is efficient.
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Citations
Semisupervised Adaptive Ladder Network for Remote Sensing Image Change Detection
TL;DR: In this article , a semisupervised adaptive ladder network (SSALN) is proposed for remote sensing image CD, which enables dual-input label-incremental architecture searching with a concise and variable structure.
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Semisupervised Adaptive Ladder Network for Remote Sensing Image Change Detection
TL;DR: Experimental results demonstrate that the proposed SSALN can promote the flow of label information through structure searching and self-circulation in the ascending network optimization; thus, it has outstanding performance on tasks of remote sensing image CD.
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A method to improve the accuracy of SAR image change detection by using an image enhancement method
TL;DR: A new image enhancement algorithm based on the combination of the wavelet domain and spatial domain and the power-law is proposed, which asserts that the algorithm retains the high-frequency details of the image while improving its sharpness.
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A Multi-Objective Community Detection Algorithm for Directed Network Based on Random Walk
Xuyun Wen,Ying Lin +1 more
TL;DR: This work formulates a multi-objective framework for community detection in directed networks and proposes a novel multi- objective evolutionary algorithm for finding efficient solutions under this framework.
Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images
Jiao Shi,Tiancheng Wu,A. K. Qin,Yu Lei,Gwanggil Joen +4 more
TL;DR: This paper proposes self-guided autoencoders (SGAE) for unsupervised change detection in heterogeneous remote sensing images, leveraging self-guided iterations to refine pseudolabels and improve discriminative feature extraction and classification performance.
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References
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
TL;DR: A novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic and the neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings.
Image change detection algorithms: a systematic survey
TL;DR: In this paper, the authors present a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling.
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation
TL;DR: By incorporating local spatial and gray information together, a novel fast and robust FCM framework for image segmentation, i.e., fast generalized fuzzy c-means (FGFCM) clustering algorithms, is proposed and can mitigate the disadvantages of FCM_S and at the same time enhances the clustering performance.
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A Robust Fuzzy Local Information C-Means Clustering Algorithm
TL;DR: A variation of fuzzy c-means (FCM) algorithm that provides image clustering that incorporates the local spatial information and gray level information in a novel fuzzy way, called fuzzy local information C-Means (FLICM).
1.1K