Dense feature pyramid fusion deep network for building segmentation in remote sensing image

Author(s):  
Qinglin Tian ◽  
Yingjun Zhao ◽  
Kai Qin ◽  
Yao Li ◽  
Xuejiao Chen
2020 ◽  
Vol 58 ◽  
pp. 116-131 ◽  
Author(s):  
Hao Zhu ◽  
Wenping Ma ◽  
Lingling Li ◽  
Licheng Jiao ◽  
Shuyuan Yang ◽  
...  

Optik ◽  
2018 ◽  
Vol 168 ◽  
pp. 127-133 ◽  
Author(s):  
Zhou Yang ◽  
Xiao-dong Mu ◽  
Feng-an Zhao

Author(s):  
Lamei Zou ◽  
Changfeng Li ◽  
Weidong Yang ◽  
Shiyang Zhou ◽  
Shiwei Nie

Author(s):  
Sumit Kaur

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 


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