A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images

By Francesca Bovolo, Silvia Marchesi, Lorenzo Bruzzone. IEEE Transactions on Geoscience and Remote Sensing, 2012

310 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/3e27355b12102dc63dfdbc8a59d8c2b69e037c31

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Citations in the corpus

GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection (2019, methodology): “Typically, based on the polar CVA, C2VA [13] describes the CD problem in a magnitude-direction 2-D representation produced by a loss compression procedure.”
Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images (2019, methodology): “The proposed hierarchical clustering approach is compared to C2VA approach [14] applied on deep change hypervectors (G).”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “S2CVA successfully extends the compressed change vector analysis method [308], which is suitable for dealing only with multispectral CD cases having few spectral channels.”
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery (2018, methodology): “Later some extensions of the CVA model have been proposed, e.g., compressed CVA (C 2 VA) [8].”
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation (2022, methodology): “Classical approaches deﬁne axiomatic, pixel-based [4–6, 20, 35] algorithms to obtain change whereas many recent approaches are data-driven [7, 12, 33, 47].”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “For example, Bovolo et al. (2007) defined a 2D feature space based on Landsat bands 351 3 and 4 allowing burnt area change to be uniquely identified from magnitude, and a single angular direction. 352 Another method used to simplify CVA direction is by applying a prior transformation to the input mu”
A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images (2013, methodology): “These methods can mainly give information about presence/absence of changes [28], [29], [31], [39] and only in specific cases about the presence of different kinds of changes [27], [40].”
A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges (2019, methodology): “A three-level hierarchical tree for modeling the multiclass CD problem (Benton County Hyperion data set) with S2CVA.”