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A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION
2017
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level
doi:10.5194/isprs-archives-xlii-2-w7-1451-2017
fatcat:w4pg3aco6faojpark43edswx64