Anju Asokan1, J. Anitha1, Bogdan Patrut2, Dana Danciulescu3, D. Jude Hemanth1,*
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 373-388, 2021, DOI:10.32604/cmc.2020.012364
- 30 October 2020
Abstract Multispectral images contain a large amount of spatial and spectral data
which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting
research area in change detection. However, many deep learning framework based
approaches do not consider both spatial and textural details into account. In order
to handle this issue, a Convolutional Neural Network (CNN) based multi-feature
extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them More >
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