scholarly journals Estimating the spatial distribution of soil total arsenic in the suspected contaminated area using UAV-Borne hyperspectral imagery and deep learning

2021 ◽  
Vol 133 ◽  
pp. 108384
Author(s):  
Lifei Wei ◽  
Yangxi Zhang ◽  
Qikai Lu ◽  
Ziran Yuan ◽  
Haibo Li ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Hyperspectral Imagery ◽  
Total Arsenic ◽  
Contaminated Area

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

Deep learning to detect built cultural heritage from satellite imagery. - Spatial distribution and size of vernacular houses in Sumba, Indonesia -

2021 ◽  
Vol 52 ◽  
pp. 171-183
Author(s):  
Fabrice Monna ◽  
Tanguy Rolland ◽  
Anthony Denaire ◽  
Nicolas Navarro ◽  
Ludovic Granjon ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Cultural Heritage ◽  
Satellite Imagery

scholarly journals Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery

2019 ◽  
Vol 11 (20) ◽  
pp. 2402 ◽  
Author(s):  
Wei ◽  
Huang ◽  
Wang ◽  
Wang ◽  
Zhou ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Decision Tree ◽  
Regression Models ◽  
Large Scale ◽  
Hyperspectral Imagery ◽  
Action Plan ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Pollution Level ◽  
Urban Habitat

The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. “Action Plan for Prevention and Control of Water Pollution” issued by the State Council shows Chinese government’s high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R2 = 0.978), and test dataset (adjusted_R2 = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations.

scholarly journals Spatial distribution of Fe and Mn in paper mill effluent affected agricultural soils in Morigaon, Assam

Agropedology ◽  
2019 ◽  
Vol 26 (1) ◽  
Author(s):  
S.K. Reza ◽  
◽  
Utpal Baruah ◽  
S.K. Singh ◽  
◽  
...  
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Spatial Dependence ◽  
Total Concentration ◽  
Gaussian Model ◽  
Spatial Prediction ◽  
Paper Mill ◽  
Paper Mill Effluent ◽  
Fe And Mn ◽  
Contaminated Area

The spatial distribution of heavy metals (Fe and Mn) in the paper mill contaminated area of Jagiroad, Assam, India were investigated using statistics, geostatistics and GIS techniques. The total concentration of Fe and Mn were determined for 188 samples collected from the contaminated area. The mean concentration of Fe (7629 mg kg1) was high. The highest and the lowest standard deviation were observed in the Fe (1749) and pH (0.81), respectively. Analysis of the isotropic variogram indicated that the Fe semivariogram was well described with the Gaussian model, with the distance of spatial dependence being 1354 m, while Mn was well described with the spherical model, with the distance of spatial dependence being 833 m. The ordinary kriging estimates of Fe and Mn maps showed that high concentrations of these metals occured in the low-lying areas like bils (lakes). For both the investigated heavy metals the prediction of goodness (G) value was greater than zero. This indicates that spatial prediction is better than assuming mean of observed value as the property value for any unsampled location. Thus the geostatistical method was spatial variability of Fe and Mn.

scholarly journals Classification and Representation Joint Learning via Deep Networks

2017 ◽  
Author(s):  
Ya Li ◽  
Xinmei Tian ◽  
Xu Shen ◽  
Dacheng Tao
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Feature Learning ◽  
Classification Performance ◽  
Local Distribution ◽  
Classification Problems ◽  
Class Label ◽  
Channel Network ◽  
Training Samples ◽  
Label Information

Deep learning has been proven to be effective for classification problems. However, the majority of previous works trained classifiers by considering only class label information and ignoring the local information from the spatial distribution of training samples. In this paper, we propose a deep learning framework that considers both class label information and local spatial distribution information between training samples. A two-channel network with shared weights is used to measure the local distribution. The classification performance can be improved with more detailed information provided by the local distribution, particularly when the training samples are insufficient. Additionally, the class label information can help to learn better feature representations compared with other feature learning methods that use only local distribution information between samples. The local distribution constraint between sample pairs can also be viewed as a regularization of the network, which can efficiently prevent the overfitting problem. Extensive experiments are conducted on several benchmark image classification datasets, and the results demonstrate the effectiveness of our proposed method.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

2021 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods: We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results: The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion: IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 396
Author(s):  
Yue Shi ◽  
Liangxiu Han ◽  
Anthony Kleerekoper ◽  
Sheng Chang ◽  
Tongle Hu
Keyword(s):  
Deep Learning ◽  
Late Blight ◽  
Hyperspectral Imagery ◽  
Feature Space ◽  
Structural Diversity ◽  
Potato Late Blight ◽  
Disease Diagnosis ◽  
Spatial Features ◽  
Late Blight Disease ◽  
Blight Disease

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.

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