scholarly journals Approaches to monitoring natural and anthropogenic objects in an analysis of the environment around large industrial facilities

2021 ◽  
Author(s):  
A.M. Konstantinova ◽  
E.A. Loupian ◽  
I.V. Balashov ◽  
A.V. Kashnitskii
Keyword(s):  
Remote Sensing ◽  
Sharp Increase ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Industrial Facilities ◽  
Satellite Systems ◽  
Sensing Data ◽  
Anthropogenic Processes ◽  
Object Oriented Approach ◽  
Oriented Approach

The paper deals with the problems of a sharp increase in the number of remote sensing satellite systems and the amount of data received, and because of this, the need to develop new approaches to the processing and use of satellite information. An “object-oriented” approach to work with remote sensing data for monitoring natural and anthropogenic processes is proposed. The paper presents a subsystem for working with objects of observation created on the basis of the “IKI-Monitoring” Center for Collective Use for the implementation of this approach. The efficiency of the subsystem is confirmed by examples of its use for the organization of remote sensing of zones of potential contamination from large industrial facilities.

Investigation of the Transformation of the Plant Ecosystem in the Vicinity of the Tsemesskaya Bay in the Black Sea Based on Remote Sensing Data

2021 ◽  
Vol 25 (6) ◽  
pp. 61-67
Author(s):  
I.V. Zen’kov ◽  
Trinh Le Hung ◽  
Yu.P. Yuronen ◽  
P.M. Kondrashov ◽  
A.A. Latyntsev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Black Sea ◽  
Remote Sensing Data ◽  
Open Pit ◽  
Open Pit Mining ◽  
The Black Sea ◽  
Industrial Facilities ◽  
Logistics Center ◽  
Sensing Data ◽  
The City

A brief description of the industrial and logistics center operating in the city of Novorossiysk on the coast of the Tsemesskaya Bay in the Black Sea is presented. According to remote sensing data, the area of open pit mining of rock dumps dumped during the development of three marl deposits for use at four cement plants was determined. According to the results of satellite imagery and analytical calculations, downward trends in changes in the density of vegetation cover in territories with natural landscapes adjacent to the territory of industrial facilities located on the coast of the Tsemesskaya Bay were revealed.

scholarly journals LANDSLIDES EXTRACTION FROM DIVERSE REMOTE SENSING DATA SOURCES USING SEMANTIC REASONING SCHEME

2016 ◽  
Vol XLI-B8 ◽  
pp. 25-31 ◽  
Author(s):  
W. Cao ◽  
X. H. Tong ◽  
S. C. Liu ◽  
D. Wang
Keyword(s):  
Expert Knowledge ◽  
Extraction Procedure ◽  
Strong Support ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Texture Features ◽  
Data Sources ◽  
Semantic Reasoning ◽  
Object Oriented Approach ◽  
Oriented Approach

Using high resolution satellite imagery to detect, analyse and extract landslides automatically is an increasing strong support for rapid response after disaster. This requires the formulation of procedures and knowledge that encapsulate the content of disaster area in the images. Object-oriented approach has been proved useful in solving this issue by partitioning land-cover parcels into objects and classifies them on the basis of expert rules. Since the landslides information present in the images is often complex, the extraction procedure based on the object-oriented approach should consider primarily the semantic aspects of the data. In this paper, we propose a scheme for recognizing landslides by using an object-oriented analysis technique and a semantic reasoning model on high spatial resolution optical imagery. Three case regions with different data sources are presented to evaluate its practicality. The procedure is designed as follows: first, the Gray Level Co-occurrence Matrix (GLCM) is used to extract texture features after the image explanation. Spectral features, shape features and thematic features are derived for semiautomatic landslide recognition. A semantic reasoning model is used afterwards to refine the classification results, by representing expert knowledge as first-order logic (FOL) rules. The experimental results are essentially consistent with the experts’ field interpretation, which demonstrate the feasibility and accuracy of the proposed approach. The results also show that the scheme has a good generality on diverse data sources.

scholarly journals Object-oriented Fuzzy Analysis of Remote Sensing Data for Bare Soil Brightness Mapping

2013 ◽  
Vol 1 (No. 3) ◽  
pp. 79-84 ◽  
Author(s):  
Borůvka Lukáš Brodský and Luboš
Keyword(s):  
Remote Sensing ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Fuzzy Analysis ◽  
Sensing Data ◽  
Soil Map

Remote sensing data have an important advantage; the data provide spatially exhaustive sampling of the area of interest instead of having samples of tiny fractions. Vegetation cover is, however, one of the application constraints in soil science. Areas of bare soil can be mapped. These spatially dense data require proper techniques to map identified patterns. The objective of this study was mapping of spatial patterns of bare soil colour brightness in a Landsat 7 satellite image in the study area of Central Bohemia using object-oriented fuzzy analysis. A soil map (1:200 000) was used to associate soil types with the soil brightness in the image. Several approaches to determine membership functions (MF) of the fuzzy rule base were tested. These included a simple manual approach, k-means clustering, a method based on the sample histogram, and one using the probability density function. The method that generally provided the best results for mapping the soil brightness was based on the probability density function with KIA = 0.813. The resulting classification map was finally compared with an existing soil map showing 72.0% agreement of the mapped area. The disagreement of 28.0% was mainly in the areas of Chernozems (69.3%).

scholarly journals AUTOMATIC EXTRACTION OF URBAN BUILT-UP AREA BASED ON OBJECT-ORIENTED METHOD AND REMOTE SENSING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 877-883 ◽  
Author(s):  
L. Li ◽  
H. Zhou ◽  
Q. Wen ◽  
T. Chen ◽  
F. Guan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Automatic Extraction ◽  
Object Oriented Method ◽  
Characteristic Parameters ◽  
Construction Land ◽  
Sensing Data ◽  
Rule Set ◽  
The City

Built-up area marks the use of city construction land in the different periods of the development, the accurate extraction is the key to the studies of the changes of urban expansion. This paper studies the technology of automatic extraction of urban built-up area based on object-oriented method and remote sensing data, and realizes the automatic extraction of the main built-up area of the city, which saves the manpower cost greatly. First, the extraction of construction land based on object-oriented method, the main technical steps include: (1) Multi-resolution segmentation; (2) Feature Construction and Selection; (3) Information Extraction of Construction Land Based on Rule Set, The characteristic parameters used in the rule set mainly include the mean of the red band (Mean R), Normalized Difference Vegetation Index (NDVI), Ratio of residential index (RRI), Blue band mean (Mean B), Through the combination of the above characteristic parameters, the construction site information can be extracted. Based on the degree of adaptability, distance and area of the object domain, the urban built-up area can be quickly and accurately defined from the construction land information without depending on other data and expert knowledge to achieve the automatic extraction of the urban built-up area. In this paper, Beijing city as an experimental area for the technical methods of the experiment, the results show that: the city built-up area to achieve automatic extraction, boundary accuracy of 2359.65 m to meet the requirements. The automatic extraction of urban built-up area has strong practicality and can be applied to the monitoring of the change of the main built-up area of city.

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