Deep-STEP: A Deep Learning Approach for Spatiotemporal Prediction of Remote Sensing Data

2016 ◽  
Vol 13 (12) ◽  
pp. 1984-1988 ◽  
Author(s):  
Monidipa Das ◽  
Soumya K. Ghosh
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Remote Sensing Data ◽  
Learning Approach ◽  
Sensing Data

scholarly journals Deep Learning Approach in Automatic Iceberg - Ship Detection with SAR Remote Sensing Data

2018 ◽  
Author(s):  
Cheng Zhan ◽  
Licheng Zhang ◽  
Zhenzhen Zhong ◽  
Sher Didi-Ooi ◽  
Youzuo Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Remote Sensing Data ◽  
Learning Approach ◽  
Sensing Data ◽  
Ship Detection ◽  
Sar Remote Sensing

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.

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.

scholarly journals Heterogeneous Change Detection on Remote Sensing Data with Self-Supervised Deep Canonically Correlated Autoencoders

2021 ◽  
Author(s):  
Federico Figari Tomenotti
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Learning Framework ◽  
Novel Approach ◽  
Image Translation ◽  
Recent Developments ◽  
Different Sources

Change detection is a well-known topic of remote sensing. The goal is to track and monitor the evolution of changes affecting the Earth surface over time. The recently increased availability in remote sensing data for Earth observation and in computational power has raised the interest in this field of research. In particular, the keywords “multitemporal” and “heterogeneous” play prominent roles. The former refers to the availability and the comparison of two or more satellite images of the same place on the ground, in order to find changes and track the evolution of the observed surface, maybe with different time sensitivities. The latter refers to the capability of performing change detection with images coming from different sources, corresponding to different sensors, wavelengths, polarizations, acquisition geometries, etc. This thesis addresses the challenging topic of multitemporal change detection with heterogeneous remote sensing images. It proposes a novel approach, taking inspiration from recent developments in the literature. The proposed method is based on deep learning - involving autoencoders of convolutional neural networks - and represents an exapmple of unsupervised change detection. A major novelty of the work consists in including a prior information model, used to make the method unsupervised, within a well-established algorithm such as the canonical correlation analysis, and in combining these with a deep learning framework to give rise to an image translation method able to compare heterogeneous images regardless of their highly different domains. The theoretical analysis is supported by experimental results, comparing the proposed methodology to the state of the art of this discipline. Two different datasets were used for the experiments, and the results obtained on both of them show the effectiveness of the proposed method.

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