Farmland Recognition of High Resolution Multispectral Remote Sensing Imagery using Deep Learning Semantic Segmentation Method

2019 ◽  
Author(s):  
Zheng Shuangpeng ◽  
Fang Tao ◽  
Huo Hong
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Remote Sensing Imagery ◽  
Multispectral Remote Sensing

A Watershed Segmentation Method with Shape-Based Merging Criterion Based on Multispectral Remote Sensing Imagery

2011 ◽  
Vol 9 (3) ◽  
pp. 1006-1013 ◽  
Author(s):  
Anzhi Yue ◽  
Jianyu Yang ◽  
Chao Zhang ◽  
Wei Su ◽  
Wenju Yun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Remote Sensing Imagery ◽  
Multispectral Remote Sensing

scholarly journals Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

2018 ◽  
Vol 10 (1) ◽  
pp. 144 ◽  
Author(s):  
Yongyang Xu ◽  
Liang Wu ◽  
Zhong Xie ◽  
Zhanlong Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Building Extraction ◽  
Remote Sensing Imagery ◽  
Very High

scholarly journals BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

2016 ◽  
Vol III-7 ◽  
pp. 83-88 ◽  
Author(s):  
M. Papadomanolaki ◽  
M. Vakalopoulou ◽  
S. Zagoruyko ◽  
K. Karantzalos
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Multispectral Data ◽  
Sensing Data ◽  
Multispectral Remote Sensing ◽  
High Level ◽  
Learning Frameworks

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the <i>AlexNet</i>, <i>AlexNet-small</i> and <i>VGG</i> models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates <i>i.e.</i>, above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

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