Approach for generating high accuracy machine learning model for high resolution geochemical map completion using remote sensing data: case study of Arizona, USA

2019 ◽  
Author(s):  
Chenhui Huang ◽  
Akinobu Shibuya
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Learning Model ◽  
High Accuracy ◽  
Sensing Data ◽  
Machine Learning Model

Sadewa satellite remote sensing data to Manggarai 1-hour water level machine learning model

2021 ◽  
Author(s):  
Jonathan Raditya Valerian ◽  
Faizal Rohmat ◽  
Hadi Kardhana ◽  
Muhammad Syahril Badri Kusuma ◽  
Muhammad Yatsrib
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Level ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Learning Model ◽  
Sensing Data ◽  
Machine Learning Model

scholarly journals USING MEDIUM RESOLUTION SATELLITE REMOTE SENSING DATA FOR COASTLINE DETECTION AND INTERPRETATION

2020 ◽  
Vol 2 (5) ◽  
pp. 56-62
Author(s):  
E.I. Volynets ◽  
◽  
A.V. Volynetc ◽  
E.A. Panidi ◽  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Resolution Image ◽  
Position Accuracy ◽  
Medium Resolution ◽  
Water Indices

Remote sensing data are widely used in coastal zones monitoring, since they provide radiometric information with the possibility to automate the data processing. Due to the lack of high resolution satellite images in free access using the medium resolution satellite images is widespread. The study is dedicated to the development of a coastline detection method based on medium resolution satellite images. It is proposed to use a semi-automatic method based on uncontrolled classification of a mid-resolution image by water indices, followed by expert refinement of classes and the use of machine learning methods. The shorelines of the eastern part of the Gulf of Finland have been extracted from Landsat 8 Level-2 image, using proposed method. The position accuracy of the generated shorelines has been analyzed using manually digitized shoreline from high-resolution image Resurs-P1 processing level 2А. The results showed that in the test areas the best output for extracting the coastline are given by the MNDWI with a fixed threshold value equal to zero. The Random Forest machine learning algorithm was used to refine the type of coastline, especially in wetlands where water indices showed poor accuracy. The study showed a significant increase in the position accuracy with the use of the algorithm. However, the accuracy of manually classified wetlands for training the model has a significant impact on the result.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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