scholarly journals Unsupervised classification of simulated magnetospheric regions

2021 ◽  
Author(s):  
Maria Elena Innocenti ◽  
Jorge Amaya ◽  
Joachim Raeder ◽  
Romain Dupuis ◽  
Banafsheh Ferdousi ◽  
...  
Keyword(s):  
Unsupervised Classification ◽  
Automatic Identification ◽  
Data Sampling ◽  
Feature Maps ◽  
Step Method ◽  
Plasma Properties ◽  
Self Organizing Maps ◽  
Data Points ◽  
Number Of Classes

Abstract. In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or opera- tional interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest. Our method is based on Self Organizing Maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well defined magnetospheric regions. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

scholarly journals Unsupervised classification of simulated magnetospheric regions

2021 ◽  
Vol 39 (5) ◽  
pp. 861-881
Author(s):  
Maria Elena Innocenti ◽  
Jorge Amaya ◽  
Joachim Raeder ◽  
Romain Dupuis ◽  
Banafsheh Ferdousi ◽  
...  
Keyword(s):  
Principal Component ◽  
Unsupervised Classification ◽  
Multistep Method ◽  
Automatic Identification ◽  
Data Sampling ◽  
Feature Maps ◽  
Plasma Properties ◽  
Self Organizing Maps ◽  
Data Points ◽  
Number Of Classes

Abstract. In magnetospheric missions, burst-mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions that could constitute the first step of a multistep method for the automatic identification of magnetospheric processes of interest. Our method is based on self-organizing maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with principal component analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well-defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with k-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

scholarly journals CHANGE DETECTION SOFTWARE USING SELF-ORGANIZING FEATURE MAPS

2012 ◽  
Vol 30 (4) ◽  
pp. 505
Author(s):  
Nilton Correia da Silva ◽  
Osmar Abílio de Carvalho Júnior ◽  
Antonio Nuno de Castro Santa Rosa ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
High Dimensional Data ◽  
Unsupervised Classification ◽  
Fine Tuning ◽  
Primary Data ◽  
High Dimensional ◽  
Feature Maps ◽  
Self Organizing

Os mapas auto-organizáveis (SOFM) consistem em um tipo de rede neural artificial que permite a conversão de dados de alta dimensão, complexos e não lineares, em simples relações geométricas com baixa dimensionalidade. Este método também pode ser utilizado para a classificação de imagens de sensoriamento remoto, pois permite a compressão de dados de alta dimensão preservando as relações topológicas dos dados primários. Este trabalho objetiva desenvolver uma metodologia eficaz para a utilização de mapas auto-organizáveis na detecção de mudanças. No presente estudo o SOFM é utilizado para a classificação não supervisionada de dados de sensoriamento remoto, considerando os seguintes atributos: espaciais (x, y), espectrais e temporais. O método é empregado na região oeste da Bahia, que teve recentemente um aumento significativo em monoculturas. Testes foram realizados com os parâmetros do SOFM com o objetivo de refinar o mapa de detecção demudanças. O SOFM possibilita uma melhor seleção de células e dos correspondentes vetores de peso, que mostram o processo de ordenação e agrupamento hierárquicodos dados. Esta informação é essencial para identificar mudanças ao longo do tempo. Um programa em linguagem C ++ do método proposto foi desenvolvido. ABSTRACT. Self-organizing feature maps (SOFM) consist of a type of artificial neural network that allows the conversion from high-dimensional data into simple geometric relationships with low-dimensionality. This method can also be used for classification of remote sensing images because it allows the compression of high dimensional data while preserving the most important topological and metric relationships of the primary data. This paper aims to develop an effective methodology forusing self-organizing maps in change detection. In this study, SOFM is used for unsupervised classification of remote sensing data, considering the following attributes: spatial (x and y), spectral and temporal. The method is tested and simulated in the western region of Bahia that has observed a significant increase in mechanized agriculture. Tests were performed with the SOFM parameters for the purpose of fine tuning a change detection map. The SOFM provides the best selection of cell and corresponding adjustment of weight vectors, which show the process of ordering and hierarchical clustering of the data. This information is essential to identify changes over time. All algorithms were implemented in C++ language.Keywords: unsupervised classification; land cover; multitemporal analysis; remote sensing

AUTOMATED ECOLOGICAL TERRAIN MORPHOLOGY CLASSIFICATION OF STRETCH OF UPPER DNISTER RIVER VALLEY

2020 ◽  
pp. 88-98
Keyword(s):  
Unsupervised Classification ◽  
River Valley ◽  
Topographic Map ◽  
Theoretical Concepts ◽  
Levels Of Detail ◽  
Gis Data ◽  
Recent Developments ◽  
Technological Developments ◽  
Number Of Classes

Modern technological developments can induce substantial changes not only in research methods, but also in theoretical concepts and approaches in Earth sciences. Recent developments in the technologies of remote sensing, GIS data processing and mapping now make possible to more directly consider ecologically relevant properties in the process of spatial units delineation. The concept of morphotop has been proposed by author meaning spatial units mapped taking into account ecologically relevant properties of terrain. It is different from the commonly used concept of natural complex in that ecological and not genetic criteria are at the base of spatial units differentiation. The ecological approach for terrain morphology classification has been applied for the 4.5 to 2 km study area located at the upper part of Dnister river valley. The 10 m spatial resolution DEM was obtained for the study area by the interpolation of digitized topographic map layers with ANUDEM algorithm. Three groups of ecologically meaningful factors of landscape differentiation have been taken into account: 1) solar radiation redistribution; 2) water and soil moisture redistribution; 3) erosion potential of terrain. For each of these, the appropriate index was proposed and derived from DEM by the respective formula. The method of iterative cluster analysis with ISODATA algorithm has been applied to these variables complemented with absolute elevation. This method distinguishes a predefined number of classes by revealing the natural groupings of data in attribute space. Arbitrary presetting the number of classes allows to classify data with the different levels of detail and to analyze the changes in classification output as a function of classification scale and detalization. The study area has been successively classified into 12 and 8 classes, with 100 algorithm iteration in each case. Each class has been given a descriptive characteristic; an average values of certain terrain morphometric parameters for each class were also calculated and given in a table. The map of the distribution of the distinguished classes was produced. Key words: terrain morphology, unsupervised classification, morphotop, ecological geomorphology.

scholarly journals Unsupervised Classification of High-Frequency Oscillations in Human Neocortical Epilepsy and Control Patients

2010 ◽  
Vol 104 (5) ◽  
pp. 2900-2912 ◽  
Author(s):  
Justin A. Blanco ◽  
Matt Stead ◽  
Abba Krieger ◽  
Jonathan Viventi ◽  
W. Richard Marsh ◽  
...  
Keyword(s):  
High Frequency ◽  
Unsupervised Classification ◽  
Data Set ◽  
High Frequency Oscillations ◽  
Unsupervised Approach ◽  
Brain States ◽  
Neocortical Epilepsy ◽  
Number Of Classes ◽  
And Control

High-frequency oscillations (HFOs) have been observed in animal and human intracranial recordings during both normal and aberrant brain states. It has been proposed that the relationship between subclasses of these oscillations can be used to identify epileptic brain. Studies of HFOs in epilepsy have been hampered by selection bias arising primarily out of the need to reduce the volume of data so that clinicians can manually review it. In this study, we introduce an algorithm for detecting and classifying these signals automatically and demonstrate the tractability of analyzing a data set of unprecedented size, over 31,000 channel-hours of intracranial electroencephalographic (iEEG) recordings from micro- and macroelectrodes in humans. Using an unsupervised approach that does not presuppose a specific number of clusters in the data, we show direct evidence for the existence of distinct classes of transient oscillations within the 100- to 500-Hz frequency range in a population of nine neocortical epilepsy patients and two controls. The number of classes we find, four (three plus one putative artifact class), is consistent with prior studies that identify “ripple” and “fast ripple” oscillations using human-intensive methods and, additionally, identifies a less examined class of mixed-frequency events.

Unsupervised classification of street architectures based on InfoGAN

2019 ◽  
Author(s):  
Ning Wang ◽  
Xianhan Zeng ◽  
Renjie Xie ◽  
Zefei Gao ◽  
Yi Zheng ◽  
...  
Keyword(s):  
Unsupervised Classification

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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