RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification.
Wadii BoulilaMokhtar SellamiMaha DrissMohammed Al-SaremMahmood SafaeiFuad A. GhalebPublished in: Comput. Electron. Agric. (2021)
Keyphrases
- remote sensing
- image classification
- convolutional neural networks
- remotely sensed data
- remote sensing images
- multispectral
- change detection
- remote sensing imagery
- image processing
- high resolution
- remote sensing data
- land cover
- image fusion
- satellite images
- image analysis
- hyperspectral
- digital image analysis
- automatic image registration
- high spatial resolution
- satellite imagery
- remotely sensed
- remotely sensed images
- remotely sensed imagery
- convolutional network
- feature extraction
- hyperspectral images
- remote sensed images
- satellite data
- multi spectral images
- geographical information systems
- environmental sciences
- earth observation
- peer to peer
- hyperspectral remote sensing
- remote sensing image processing
- climate change
- earth science
- high resolution satellite images
- land cover classification
- spatial resolution
- data streams
- high quality