@Article{jnm.2020.014115,
AUTHOR = {Aziguli Wulamu, Jingyue Sang, Dezheng Zhang, Zuxian Shi},
TITLE = {Robust Cultivated Land Extraction Using Encoder-Decoder},
JOURNAL = {Journal of New Media},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {149--155},
URL = {http://www.techscience.com/JNM/v2n4/40919},
ISSN = {2579-0129},
ABSTRACT = {Cultivated land extraction is essential for sustainable development and 
agriculture. In this paper, the network we propose is based on the encoderdecoder structure, which extracts the semantic segmentation neural network of 
cultivated land from satellite images and uses it for agricultural automation 
solutions. The encoder consists of two part: the first is the modified Xception, it 
can used as the feature extraction network, and the second is the atrous 
convolution, it can used to expand the receptive field and the context information 
to extract richer feature information. The decoder part uses the conventional 
upsampling operation to restore the original resolution. In addition, we use the 
combination of BCE and Loves-hinge as a loss function to optimize the 
Intersection over Union (IoU). Experimental results show that the proposed 
network structure can solve the problem of cultivated land extraction in 
Yinchuan City.},
DOI = {10.32604/jnm.2020.014115}
}