Multiscale Morphological Compressed Change Vector Analysis for Unsupervised Multiple Change Detection

@article{Liu2017MultiscaleMC,
  title={Multiscale Morphological Compressed Change Vector Analysis for Unsupervised Multiple Change Detection},
  author={Sicong Liu and Qian Du and Xiaohua Tong and Alim Samat and Lorenzo Bruzzone and Francesca Bovolo},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2017},
  volume={10},
  pages={4124-4137}
}
A novel multiscale morphological compressed change vector analysis (M2C2VA) method is proposed to address the multiple-change detection problem (i.e., identifying different classes of changes) in bitemporal remote sensing images. The proposed approach contributes to extend the state-of-the-art spectrum-based compressed change vector analysis (C2VA) method by jointly analyzing the spectral-spatial change information. In greater details, reconstructed spectral change vector features are built… CONTINUE READING

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