A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment
By Francesca Bovolo, Lorenzo Bruzzone. IEEE Transactions on Geoscience and Remote Sensing, 2007
298 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Artificial Intelligence, Media Technology
View Paper: https://www.semanticscholar.org/paper/3b69ea64bc27b3e27065597fb2559c71a79358b6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Turgay Çelik
- Francesca Bovolo
- Lorenzo Bruzzone
- Dominik Brunner
- Guido Lemoine
- Sudipan Saha
- Daniele Marinelli
- Sicong Liu
In Artificial Intelligence
- Lorenzo Bruzzone
- Francesca Bovolo
- Turgay Çelik
- Sudipan Saha
- Sicong Liu
- Dominik Brunner
- Guido Lemoine
- Daniele Marinelli
In Media Technology
- Lorenzo Bruzzone
- Francesca Bovolo
- Turgay Çelik
- Sicong Liu
- Sudipan Saha
- Daniele Marinelli
- Dominik Brunner
- Guido Lemoine
Citations in the corpus
- A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images (2013, background): “The use of VHR images for identifying damages caused by natural disasters is particularly important for a detailed evaluation of the effects of such disasters [73], [80]–[83].”
- Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images (2019, methodology): “The idea is similar in essence to the idea of image split based thresholding proposed in [63].”
- Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery (2010, methodology): “ω([16]) d is the building that appeared undamaged in the post-QB scene and damaged in the post-WV scene.”
- Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, background): “Change detection techniques are extensively utilized in various applications [15] including; disaster assessment [16], environmental monitoring [17], land management [18] and urban change analysis [19], etc.”
- Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering (2009, methodology): “Several unsupervised change detection techniques have been proposed in the literature falling in these categories [2]–[4] which use complicated data modeling and parameter estimation.”
- A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges (2019, methodology): “CD techniques have been developed and successfully used in different remote sensing applications, such as agriculture and forestry monitoring [12], [13], natural-disaster mapping [14], [15],”