An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images

By Yakoub Bazi, Lorenzo Bruzzone, Farid Melgani. IEEE Transactions on Geoscience and Remote Sensing, 2005

751 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f65ccb1d997524c44b59cb2def7911e9e80a2d50

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Citations in the corpus

Deep-learning-based information mining from ocean remote-sensing imagery (2020, methodology): “The traditional ways of coastal-inundation mapping from SAR images include: histogram thresholding [50], active contour segmentation [51], region growing [52], change detection [53] and statistical classification [54].”
Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery (2010, methodology): “Unsupervised change detection methods using multitemporal SAR data have been proposed in [14]–[16].”
A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis (2007, methodology): “Whether it is for the case of a single scale or for the case of a multiscale analysis, the statistics of the change indicators could be used in order to propose adaptive Bayesian thresholding techniques, as done in [13].”
A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images (2018, methodology): “Thresholding methods are often used in this process [23], [24].”
A detail-preserving scale-driven approach to change detection in multitemporal SAR images (2005, methodology): “However, despite the complexity of data processing, SAR sensors have important properties at the operational level, since they are capable of acquiring data in all weather conditions and are not affected by cloud cover or different sunlight conditions (see [9] for greater details on the importance…”
Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery (2005, methodology): “On the other hand, very good results were achieved on prefiltered images (with error rates TABLE VI EXPERIMENT II: CHANGE-DETECTION PERFORMANCES PROVIDED BY THE THREE VERSIONS OF GKIT AND BY THE GG LOG-RATIO METHOD PROPOSED IN [21] (GGLR-GKIT)”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, methodology): “Among the parametric models that have been proposed to characterize the classconditional statistics fð Þ and gð Þ for change detection purposes, we recall Nakagami-ratio, Weibull-ratio, lognormal [148], and generalized Gaussian distributions [147], and dictionary-based approaches [145] in the case o”
Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016, methodology): “non-Gaussian distribution of the amplitude values of SAR images into account [1].”
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering (2012, background): “However, SAR sensors are independent of atmospheric and sunlight conditions, which make the change detection in SAR images still attractive [4]–[8].”
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images (2011, methodology): “, Gaussian [5], generalized Gaussian [1] and mixture of Gaussians [3].”
Multitask learning for large-scale semantic change detection (2018, methodology): “Many of these methods automatically analyse the data in difference images and detect patterns that correspond to changes (Bazi et al., 2005; Bruzzone and Prieto, 2000).”