An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images

By Lorenzo Bruzzone, Diego Fernández-Prieto. IEEE Transactions on Image Processing, 2002

442 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/44afcd789b63bbee6436a9931f64522563f1457f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Gustau Camps-Valls
2. Lorenzo Bruzzone
3. Francesca Bovolo
4. Jon Atli Benediktsson
5. Luis Gómez-Chova
6. Mattia Marconcini
7. Farid Melgani
8. Yakoub Bazi

In Ecology

1. Lorenzo Bruzzone
2. Francesca Bovolo
3. Mattia Marconcini
4. Allan Aasbjerg Nielsen
5. Farid Melgani
6. Maoguo Gong
7. Gustau Camps-Valls
8. Yakoub Bazi

In Media Technology

1. Lorenzo Bruzzone
2. Francesca Bovolo
3. Gustau Camps-Valls
4. Jon Atli Benediktsson
5. Luis Gómez-Chova
6. Mattia Marconcini
7. Yakoub Bazi
8. Maoguo Gong

Citations in the corpus

• Fuzzy clustering algorithms for unsupervised change detection in remote sensing images (2011, methodology): “, Gaussian [5], generalized Gaussian [1] and mixture of Gaussians [3].”
• An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images (2005, methodology): “This approach has been extended in [ 8 ] by adopting a semiparametric technique that exploits the reduced Parzen estimation (RPE) technique for a better modeling of the density functions associated with changed and unchanged pixels.”
• Image change detection algorithms: a systematic survey (2005, methodology): “Bruzzone and Prieto [69] noted that while the variances estimated as above may serve as good initial guesses, using them in a decision rule may result in a false alarm rate different from the desired value.”
• A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure (2008, methodology): “in change detection on multispectral images [8], [9], [15].”
• The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data (2007, background): “The spatial aspect is dealt with elegantly in a Markov random field setting in [33] and [34].”
• Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection (2008, methodology): “In remote sensing, change identification traditionally subtracts the subsequent images and then applies a threshold that is tuned either heuristically or under other more sophisticated criteria [5], [20], [21], [23].”
• Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016, methodology): “preprocessing; 2) generation of a DI; and 3) analysis of the DI [42].”
• CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS (2018, background): “The PBCD category contains the direct, transform-based, classificationbased and learning-based comparison of images at the pixel level (Wiemker, 1997; Bruzzone and Fernandez-Prieto, 2002; Ghosh et al., 2007; Benedek and Szir'anyi, 2009; Singh et al., 2014; Rubis et. al., 2016).”
• A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment (2007, background): “that are proposed in the literature (e.g., generalized Kittler and Illingworth (KI) algorithm [7], [9], [10] and generalized Expectation‐Maximization-based algorithm [ 8 ], [17], [18]).”