scholarly journals Possibility of using satellite-based monitoring for large-scale mapping and research of dynamics of mud volcanic landscapes

2021 ◽  
Vol 946 (1) ◽  
pp. 012040
Author(s):  
A V Kopanina ◽  
K A Shvidskaya
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Automatic Classification ◽  
Mud Volcano ◽  
Satellite Monitoring ◽  
Eruptive Activity ◽  
Color Channel ◽  
Remote Probing ◽  
Sentinel 2A

Abstract Currently Earth remote probing to study vegetation dynamics and monitor volcanic activity is of great scientific interest. The purpose of this study is to create a large-scale outline map of Yuzhno-Sakhalinsk mud volcano which will include the topography objects, mud fields of eruptions of various years and gryphons, and to perform semi-automatic classification of Yuzhno-Sakhalinsk mud volcano. Work was performed with QGIS software using the following modules: «QuickMapServices», «Freehandrastergeoreference», «LatLanTools», and «Semi-AutomaticClassificationPlugin». We developed an outline map of Yuzhno-Sakhalinsk mud volcano on a scale of 1:10000, which shows how the mud flows have changed directions over the last 70 years, as well as mud fields have been formed over the last 20 years. Using semi-automatic classification of satellite images from Sentinel-2A satellite in various color channel sets, we obtained two premaps of Yuzhno-Sakhalinsk mud volcano vegetation on a scale of 1:50 000. Satellite monitoring of YuSMV activity allows us to track the eruptive activity of the volcano, and assess its impact on vegetation.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

scholarly journals Automatic classification of histopathological diagnoses for building a large scale tissue catalogue

2016 ◽  
Vol 7 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Robert Reihs ◽  
Heimo Müller ◽  
Stefan Sauer ◽  
Kurt Zatloukal
Keyword(s):  
Large Scale ◽  
Automatic Classification

Automatic Classification of Retail Spaces from a Large Scale Topographic Database

2011 ◽  
Vol 15 (3) ◽  
pp. 291-307 ◽  
Author(s):  
William A Mackaness ◽  
Omair Z Chaudhry
Keyword(s):  
Large Scale ◽  
Automatic Classification

Geoinformation carthography of the landscapes of the Mondy depression

2018 ◽  
Vol 934 (4) ◽  
pp. 23-30 ◽  
Author(s):  
E.A. Istomina ◽  
E.V. Ovchinnikova
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Digital Elevation ◽  
Elevation Model ◽  
Landsat Satellite ◽  
Landscape Map

A method of typological mapping of landscapes with the use of Landsat satellite images and the digital elevation model SRTM, as well as the method of factorial-dynamic classification of landscapes, was developed and a large-scale landscape map of the Mondy basin was created. At the first stage, the image was automatically classified using the neural network classification method, resulting in a picture divided into 11 classes. The resulting classified image was smoothed to remove the mosaic effect and translated into a vector map. For each unit obtained as a result of the classification of the satellite image, the following parameters were calculated by means of spatial analysis in the GIS

scholarly journals Multiobjects Association and Abnormal Behavior Detection for Massive Data Analysis in Multisensor Monitoring Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ke Bao ◽  
Yourong Ding
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Monitoring Network ◽  
Automatic Classification ◽  
Abnormal Behavior ◽  
Statistical Dependence ◽  
Massive Data ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

With the rapid increase in the number of large-scale distributed cameras and the rapid increase in the monitoring range of the camera network, how to accurately recognize and analyze abnormal behavior is still a challenging problem. In addition, the appearance of moving objects between different cameras without overlapping fields of view undergoes significant changes, making it difficult to obtain accurate association Therefore, multiobjects association and abnormal behavior detection for massive data analysis in multisensor monitoring network are proposed in this paper, which firstly uses belief propagation to associate multiple objects, extracts the object’s behavior trajectory characteristics, and then builds a long short-term memory classification network to realize automatic classification of abnormal behaviors. Multiobject association fully considers the timing correlation and object detection probability, as well as the statistical dependence of the measurement on the association matrix. The experimental results show that our proposed method can achieve a high classification accuracy and sensitivity, which meets the requirements of automatic classification of abnormal behavior in complex monitoring network. This further shows that this research has practical application value.

scholarly journals INVESTIGATING THE POTENTIAL OF DEEP NEURAL NETWORKS FOR LARGE-SCALE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE IMAGES

2017 ◽  
Vol IV-1/W1 ◽  
pp. 183-190 ◽  
Author(s):  
T. Postadjian ◽  
A. Le Bris ◽  
H. Sahbi ◽  
C. Mallet
Keyword(s):  
Neural Networks ◽  
Land Cover ◽  
Large Scale ◽  
Satellite Images ◽  
Superior Performance ◽  
Map Generation ◽  
Land Cover Map ◽  
Very High ◽  
Scale Classification

Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely <i>buildings, roads, water, crops, vegetated areas</i>) by exploiting existing VHR land-cover maps for training.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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