Image segmentation of clouds based on deep learning

2020 ◽  
Vol 2020 (48) ◽  
pp. 72-78
Author(s):  
B.P. Rusyn ◽  
◽  
O.A. Lutsyk ◽  
R.Ya. Kosarevych ◽  
V.V. Korniy ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Weather Forecasting ◽  
Quality Criteria ◽  
Time Operation ◽  
Real Time Operation ◽  
Automated Processing ◽  
Segmentation Of Images ◽  
Remote Sensing Methods

The paper is devoted to the development of the methods for segmentation of images of atmospheric clouds, which are obtained by remote sensing methods using aircraft or satellite onboard systems. The proposed approach is some extent further improvement of the convolutional neural network of the U-net type. The uses known quality criteria for segmentation, which allows us to compare the proposed approach with already known methods in the field of segmentation of images of atmospheric clouds. A large number of experiments on real images shows the feasi-bility of using the proposed method of segmentation for automated processing with the require-ments for real-time operation. Applied use of the results is possible in the tasks of monitoring and classification for weather forecasting, agriculture, and other areas related to observations of atmospheric clouds.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data

2018 ◽  
Vol 7 (11) ◽  
pp. 418 ◽  
Author(s):  
Tian Jiang ◽  
Xiangnan Liu ◽  
Ling Wu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Vegetation Index ◽  
Southern China ◽  
Series Data ◽  
Support Vector ◽  
Complex Landscape

Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classification approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classification. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to fine tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classification by remote sensing and deep learning technique in the future study.

scholarly journals Contribution of the Black Sea observing system to ECOOP

2011 ◽  
Vol 8 (4) ◽  
pp. 1695-1722 ◽  
Author(s):  
G. K. Korotaev ◽  
V. L. Dorofeev ◽  
S. V. Motyzhev ◽  
V. N. Belokopytov ◽  
A. Palazov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Black Sea ◽  
The Black Sea ◽  
Past Century ◽  
Network Integration ◽  
The Past ◽  
Time Operation ◽  
Real Time Operation ◽  
Set Up

Abstract. Regular observations in the Black Sea basin started in the past century, and quite good multidisciplinary observing system operated in the 70–80ies based on the ship observations. Modern oceanographic observing system in the basin is built according to the GOOS principles. It includes space remote sensing observations, data of free floating buoys and costal observational network. Integration of the observing system and its real-time operation were started within the framework of the FP5 ARENA project and later were improved during the FP6 ASCABOS project. The coastal observing system which includes time series from the coastal platforms and multidisciplinary surveys of the coastal areas fulfilled by the research vessels was set up during the ECOOP. Paper describes all components of the Black Sea observing system operated during the ECOOP project and its applications in the framework of the project.

scholarly journals Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta

2019 ◽  
Vol 11 (9) ◽  
pp. 1006 ◽  
Author(s):  
Quanlong Feng ◽  
Jianyu Yang ◽  
Dehai Zhu ◽  
Jiantao Liu ◽  
Hao Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Yellow River ◽  
Rapid Development ◽  
Land Cover Classification ◽  
Coastal Land

Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

scholarly journals Weather Nowcasting Using Deep Learning Techniques

2021 ◽  
Author(s):  
Makhamisa Senekane ◽  
Mhlambululi Mafu ◽  
Molibeli Benedict Taele
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Weather Forecasting ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Precipitation Forecasting ◽  
Learning Techniques

Weather variations play a significant role in peoples’ short-term, medium-term or long-term planning. Therefore, understanding of weather patterns has become very important in decision making. Short-term weather forecasting (nowcasting) involves the prediction of weather over a short period of time; typically few hours. Different techniques have been proposed for short-term weather forecasting. Traditional techniques used for nowcasting are highly parametric, and hence complex. Recently, there has been a shift towards the use of artificial intelligence techniques for weather nowcasting. These include the use of machine learning techniques such as artificial neural networks. In this chapter, we report the use of deep learning techniques for weather nowcasting. Deep learning techniques were tested on meteorological data. Three deep learning techniques, namely multilayer perceptron, Elman recurrent neural networks and Jordan recurrent neural networks, were used in this work. Multilayer perceptron models achieved 91 and 75% accuracies for sunshine forecasting and precipitation forecasting respectively, Elman recurrent neural network models achieved accuracies of 96 and 97% for sunshine and precipitation forecasting respectively, while Jordan recurrent neural network models achieved accuracies of 97 and 97% for sunshine and precipitation nowcasting respectively. The results obtained underline the utility of using deep learning for weather nowcasting.

scholarly journals Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing

2020 ◽  
Vol 2 (2) ◽  
pp. 294-307 ◽  
Author(s):  
Xiaoling Deng ◽  
Zejing Tong ◽  
Yubin Lan ◽  
Zixiao Huang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wood Nematode ◽  
Dead Tree ◽  
Pine Wood ◽  
Tree Detection ◽  
Pine Wilt

Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection.

scholarly journals Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1576 ◽  
Author(s):  
Li Zhu ◽  
Lianghao Huang ◽  
Linyu Fan ◽  
Jinsong Huang ◽  
Faming Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Environmental Factors ◽  
Recurrent Neural Network ◽  
Landslide Susceptibility ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Predictive Rate

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

Classification of EgyptSat-1 Images Using Deep Learning Methods

2020 ◽  
Vol 10 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Hatem Keshk ◽  
Xu-Cheng Yin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Average Accuracy

Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing. Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted. Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

Sign in / Sign up

Export Citation Format

Share Document