Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery

336 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “DL also has been used in remote sensing for land and crop detection and classiﬁcation [109] [110] [111].”
Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention (2021, background): “11, we investigate several positions for the attention branch including MBConv blocks 9,14, 19, 25, and the last 27th block.”
Fusion of Deep Convolutional Neural Networks for Land Cover Classification of High-Resolution Imagery (2017, methodology): “Additionally, approaches such as [4] have demonstrated advanced data augmentation techniques for training DCNN structures.”
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images (2018, methodology): “Scott et al.[48] applied DCNN-based approach on the UCM dataset and obtained a best accuracy of 98.5% with ResNet-50.”
Land cover 2.0 (2018, background): “…(e.g. Janssen et al. 1994; Foody 2002, Stehman 2009; Stehman 2013) with increased emphasis on calibration data needs relative to speci ﬁ c data inputs (i.e. time series, Pelletier et al. 2017) and classi ﬁ cation approaches (e.g. Millard and Richardson 2015; Mellor et al. 2015; Scott et al. 2017).”