scholarly journals AIRBORNE, OPTICAL REMOTE SENSING OF METHANE AND ETHANE FOR NATURAL GAS PIPLINE LEAK DETECTION

2004 ◽  
Author(s):  
Jerry Myers
Keyword(s):  
Remote Sensing ◽  
Natural Gas ◽  
Leak Detection ◽  
Optical Remote Sensing

Natural Gas Pipeline Leak Detection System for Laser Remote Sensing and Calibration

2010 ◽  
Vol 37 (11) ◽  
pp. 2912-2917
Author(s):  
汪磊 Wang Lei ◽  
谈图 Tan Tu ◽  
王贵师 Wang Guishi ◽  
高晓明 Gao Xiaoming
Keyword(s):  
Remote Sensing ◽  
Natural Gas ◽  
Detection System ◽  
Leak Detection ◽  
Gas Pipeline ◽  
Natural Gas Pipeline ◽  
Laser Remote Sensing

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Design of global-contextual detection model for optical remote sensing targets

2020 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
ZHANG Rui-yan ◽  
◽  
JIANG Xiu-jie ◽  
AN Jun-she ◽  
CUI Tian-shu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Detection Model
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