scholarly journals Land cover classification of satellite images using cooperative learning neural networks.

1995 ◽  
Vol 34 (1) ◽  
pp. 71-80
Author(s):  
Makoto KAWAMURA ◽  
Yuji TSUJIKO
Keyword(s):  
Neural Networks ◽  
Cooperative Learning ◽  
Land Cover ◽  
Satellite Images ◽  
Land Cover Classification

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

2017 ◽  
Vol 14 (4) ◽  
pp. 549-553 ◽  
Author(s):  
Grant J. Scott ◽  
Matthew R. England ◽  
William A. Starms ◽  
Richard A. Marcum ◽  
Curt H. Davis
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Deep Convolutional Neural Networks ◽  
High Resolution Imagery

scholarly journals LAND COVER CLASSIFICATION OF SATELLITE IMAGES USING CONTEXTUAL INFORMATION

2013 ◽  
Vol II-3/W1 ◽  
pp. 1-6 ◽  
Author(s):  
B. Fröhlich ◽  
E. Bach ◽  
I. Walde ◽  
S. Hese ◽  
C. Schmullius ◽  
...  
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Contextual Information ◽  
Land Cover Classification

scholarly journals TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 139-146
Author(s):  
C. Yang ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Aerial Images ◽  
Infrared Band ◽  
Rgb Images ◽  
Geospatial Databases

<p><strong>Abstract.</strong> Land use and land cover are two important variables in remote sensing. Commonly, the information of land use is stored in geospatial databases. In order to update such databases, we present a new approach to determine the land cover and to classify land use objects using convolutional neural networks (CNN). High-resolution aerial images and derived data such as digital surface models serve as input. An encoder-decoder based CNN is used for land cover classification. We found a composite including the infrared band and height data to outperform RGB images in land cover classification. We also propose a CNN-based methodology for the prediction of land use label from the geospatial databases, where we use masks representing object shape, the RGB images and the pixel-wise class scores of land cover as input. For this task, we developed a two-branch network where the first branch considers the whole area of an image, while the second branch focuses on a smaller relevant area. We evaluated our methods using two sites and achieved an overall accuracy of up to 89.6% and 81.7% for land cover and land use, respectively. We also tested our methods for land cover classification using the Vaihingen dataset of the ISPRS 2D semantic labelling challenge and achieved an overall accuracy of 90.7%.</p>

scholarly journals Land Cover Classification of RapidEye Satellite Images Using Tesseled Cap Transformation (TCT)

2017 ◽  
Vol 33 (1) ◽  
pp. 79-88 ◽  
Author(s):  
Hogyung Moon ◽  
Taeyoung Choi ◽  
Guhyeok Kim ◽  
Nyunghee Park ◽  
Honglyun Park ◽  
...  
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Land Cover Classification
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