scholarly journals A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans

2021 ◽  
Vol 13 (19) ◽  
pp. 4018
Author(s):  
Tianxia Jia ◽  
Yonglin Zhang ◽  
Rencai Dong
Keyword(s):  
Fuzzy Logic ◽  
Natural Waters ◽  
Hyperspectral Data ◽  
Global Ocean ◽  
Trophic State Index ◽  
Water Type ◽  
Data Set ◽  
Water Classification ◽  
Satellite Sensors

The classification of natural waters is a way to generalize and systematize ocean color science. However, there is no consensus on an optimal water classification template in many contexts. In this study, we conducted an unsupervised classification of the PACE (Plankton, Aerosols, Cloud, and Ocean Ecosystem) synthetic hyperspectral data set, divided the global ocean waters into 15 classes, then obtained a set of fuzzy logic optical water type schemes (abbreviated as the U-OWT in this study) that were tailored for several multispectral satellite sensors, including SeaWiFS, MERIS, MODIS, OLI, VIIRS, MSI, and OLCI. The consistency analysis showed that the performance of U-OWT on different satellite sensors was comparable, and the sensitivity analysis demonstrated the U-OWT could resist a certain degree of input disturbance on remote sensing reflectance. Compared to existing ocean-aimed optical water type schemes, the U-OWT can distinguish more mesotrophic and eutrophic water classes. Furthermore, the U-OWT was highly compatible with other water classification taxonomies, including the trophic state index, the multivariate absorption combinations, and the Forel-Ule Scale, which indirectly demonstrated the potential for global applicability of the U-OWT. This finding was also helpful for the further conversion and unification of different water type taxonomies. As the fundamental basis, the U-OWT can be applied to many oceanic fields that need to be explored in the future. To promote the reproducibility of this study, an IDL®-based standalone U-OWT calculation tool is freely distributed.

scholarly journals Evaluation of satellite sensors to compute Chlorophyll-a concentration in the Northeastern Arabian Sea: A validation approach

2021 ◽  
Author(s):  
Vinaya Kumar Vase ◽  
Nakhawa Ajay ◽  
Rajan Kumar ◽  
Jayasankar Jayaraman ◽  
Prathibha Rohit
Keyword(s):  
Arabian Sea ◽  
Ocean Color ◽  
Geographic Region ◽  
Global Ocean ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Color Sensors ◽  
Satellite Sensors ◽  
Fine Tune

Abstract The primary productivity of an aquatic system like the Arabian Sea is broadly determined by the concentration of Chlorophyll-a (Chl-a/Ca) pigment. The present study is essaying to validate the Chl-a data set retrieved from the prominent ocean color sensors (OC3M - MODIS, OC-OCM2, and OC3V-VIIRS) with sea truth data, collected from 204 stations for a three year period (2015–2017). The in-situ concentrations of Chl-a depict the geographic region under the mesotrophic and eutrophic spans with a mean of 1.36 mg m-3 ((0.1 > Ca>1.0 mg m-3). The ratio of CaOCM2/CaIn-situ was 0.97 ± 0.27 mg m-3 (n = 199), but the ratios were higher with CaVIIRS/CaIn-situ is 1.75 ± 0.79 mg m-3 (n = 170) and CaMODIS/CaIn-situ is 2.53 ± 1.42 mg m-3 (n = 158). The coefficient of determination proclaims a moderate significant relationship for MODIS (R2 = 0.36; p < 0.001), followed OCM2 (R2 = 0.32; p < 0.001) and VIIRS (R2 = 0.19; p < 0.001). The OCM2 showed the lowest RMSE as 0.13, which is relatively lower than the reference error limit by global ocean color missions at 0.35. In overall performance among three algorithms, the OCM2 will provide a better estimation of Chl-a with a prediction of 32% accuracy and 34.37 % of bias. The log bias values for MODIS (0.35) and VIIRS (0.20) algorithms indicating the overestimation of Chl-a with in-situ Chl-a, but the OCM2 algorithm is suitable in the region with a negligible bias (-0.03). The biogeochemical processes and ecosystem characteristics are dynamic from region to region, as yet in its urgent need to validate global sensors to fine-tune the regional algorithms periodically.

Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

2019 ◽  
Author(s):  
M Maktabi ◽  
H Köhler ◽  
R Thieme ◽  
JP Takoh ◽  
SM Rabe ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Data ◽  
Tissue Classification

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Using class-based feature selection for the classification of hyperspectral data

2011 ◽  
Vol 32 (15) ◽  
pp. 4311-4326 ◽  
Author(s):  
Yasser Maghsoudi ◽  
Mohammad Javad Valadan Zoej ◽  
Michael Collins
Keyword(s):  
Feature Selection ◽  
Hyperspectral Data ◽  
Selection For

scholarly journals Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1988
Author(s):  
Minqi Hu ◽  
Ronghua Ma ◽  
Zhigang Cao ◽  
Junfeng Xiong ◽  
Kun Xue
Keyword(s):  
Yangtze River ◽  
Trophic State ◽  
Inland Waters ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Type I ◽  
Water Type ◽  
Type Ii ◽  
Turbid Water ◽  
State Index

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.

scholarly journals ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

2021 ◽  
Author(s):  
Xiongzhi Ai ◽  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Yu Fu
Keyword(s):  
Papillary Carcinoma ◽  
Network Model ◽  
Reliability And Validity ◽  
Thyroid Papillary Carcinoma ◽  
Data Set ◽  
Ultrasonic Image ◽  
First Choice ◽  
Thyroid Papillary ◽  
Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

Classification of hyperspectral data in urban areas

1998 ◽  
Author(s):  
Bing Zhang ◽  
Jiangui Liu ◽  
Xiangjun Wang ◽  
Changshan Wu
Keyword(s):  
Urban Areas ◽  
Hyperspectral Data
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