Implications of Remote Sensing Data Analysis Using Deep Learning Techniques

2018 ◽  
Vol 15 (9) ◽  
pp. 2857-2862
Author(s):  
M Kavitha ◽  
R Gayathri
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Data Analysis ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Learning Techniques

scholarly journals Improving the Efficiency of Deep Learning Methods in Remote Sensing Data Analysis: Geosystem Approach

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 179516-179529
Author(s):  
Stanislav A. Yamashkin ◽  
Anatoliy A. Yamashkin ◽  
Victor V. Zanozin ◽  
Milan M. Radovanovic ◽  
Alexander N. Barmin
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Data Analysis ◽  
Remote Sensing Data ◽  
Learning Methods ◽  
Sensing Data

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

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