Drought Prediction and Analysis of Water level based on satellite images Using Deep Convolutional Neural Network

2021 ◽  
Author(s):  
J. Balajee ◽  
M. A. Saleem Durai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Water Level ◽  
Satellite Images ◽  
Deep Convolutional Neural Network ◽  
Drought Prediction

scholarly journals Deep Convolutional Neural Network for Mapping Smallholder Agriculture Using High Spatial Resolution Satellite Image

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2398 ◽  
Author(s):  
Bin Xie ◽  
Hankui K. Zhang ◽  
Jie Xue
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Spatial Information ◽  
Deep Convolutional Neural Network ◽  
Crop Types ◽  
Pooling Strategy

In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically explore high level spatial information and thus is promising for such tasks. This study tried to evaluate different CNN structures for classification of four smallholder agricultural landscapes in Heilongjiang, China using pan-sharpened 2 m GaoFen-1 (meaning high resolution in Chinese) satellite images. CNN with three pooling strategies: without pooling, with max pooling and with average pooling, were evaluated and compared with random forest. Two different numbers (~70,000 and ~290,000) of CNN learnable parameters were examined for each pooling strategy. The training and testing samples were systematically sampled from reference land cover maps to ensure sample distribution proportional to the reference land cover occurrence and included 60,000–400,000 pixels to ensure effective training. Testing sample classification results in the four study areas showed that the best pooling strategy was the average pooling CNN and that the CNN significantly outperformed random forest (2.4–3.3% higher overall accuracy and 0.05–0.24 higher kappa coefficient). Visual examination of CNN classification maps showed that CNN can discriminate better the spectrally similar crop types by effectively exploring spatial information. CNN was still significantly outperformed random forest using training samples that were evenly distributed among classes. Furthermore, future research to improve CNN performance was discussed.

Deep Convolutional Neural Network for Classifying Satellite Images with Heterogeneous Spatial Resolutions

2021 ◽  
pp. 519-530
Author(s):  
Mateus de Souza Miranda ◽  
Valdivino Alexandre de Santiago ◽  
Thales Sehn Körting ◽  
Rodrigo Leonardi ◽  
Moisés Laurence de Freitas
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Deep Convolutional Neural Network

scholarly journals Scalable Flood Level Trend Monitoring with Surveillance Cameras using a Deep Convolutional Neural Network

2019 ◽  
Author(s):  
Matthew Moy de Vitry ◽  
Simon Kramer ◽  
Jan Dirk Wegner ◽  
João P. Leitão
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Water Level ◽  
Deep Convolutional Neural Network ◽  
Water Level Fluctuations ◽  
New Approach ◽  
Surveillance Camera ◽  
Camera Systems ◽  
Lighting Conditions ◽  
Flood Level

Abstract. In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that is needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (SOFI) as a proxy for water level fluctuations visible from a surveillance camera’s viewpoint. To demonstrate the approach, we trained the DCNN on 1281 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had on average a 75 % correlation to the actual water level fluctuation. By retraining the DCNN with a few frames from a given video, performance for that video is further increased to 85 %. The results suggest that the approach can be used with almost any surveillance footage without the need for on-site camera calibration, making it cheap, highly scalable, and retroactively applicable to archived surveillance footage.

scholarly journals Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

2019 ◽  
Vol 23 (11) ◽  
pp. 4621-4634 ◽  
Author(s):  
Matthew Moy de Vitry ◽  
Simon Kramer ◽  
Jan Dirk Wegner ◽  
João P. Leitão
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Water Level ◽  
Deep Convolutional Neural Network ◽  
Water Level Fluctuations ◽  
New Approach ◽  
Surveillance Camera ◽  
Camera Systems ◽  
Lighting Conditions ◽  
Flood Level

Abstract. In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that are needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water level fluctuations visible from a surveillance camera's viewpoint. To demonstrate the approach, we trained the DCNN on 1218 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had a 75 % correlation to the actual water level fluctuation on average. By retraining the DCNN with a few frames from a given video, the correlation is increased to 85 % on average. The results confirm that the approach is versatile, with the potential to be applied to a variety of surveillance camera models and flooding situations without the need for on-site camera calibration. Thanks to this flexibility, this approach could be a cheap and highly scalable alternative to conventional sensing methods.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Deep Convolutional Neural Network applied to Chagas Disease Parasitemia Assessment

2020 ◽  
Author(s):  
André Pereira ◽  
Alexandre Pyrrho ◽  
Daniel Vanzan ◽  
Leonardo Mazza ◽  
José Gabriel Gomes
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Chagas Disease ◽  
Deep Convolutional Neural Network
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