SURVEY OF THE PROBLEM OF EARTH REMOTE SENSING DATA IN ORDER TO INCREASE SATELLITE IMAGES DETAILS

2020 ◽  
Author(s):  
S.M. Larionov ◽  
◽  
A.E. Moskvitin ◽  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Sensing Data

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Summaries of certain spatial patterns retrieved from multidate remote-sensing data

2004 ◽  
Vol 2004 (2) ◽  
pp. 287-300
Author(s):  
Hema Nair
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Spatial Patterns ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Geographic Area ◽  
Image Mining ◽  
Sensing Data

This paper presents an approach to describe patterns in remote-sensed images utilising fuzzy logic. The truth of a linguistic proposition such as “Y isF” can be determined for each pattern characterised by a tuple in the database, where Y is the pattern andFis a summary that applies to that pattern. This proposition is formulated in terms of primary quantitative measures, such as area, length, perimeter, and so forth, of the pattern. Fuzzy descriptions of linguistic summaries help to evaluate the degree to which a summary describes a pattern or object in the database. Techniques, such as clustering and genetic algorithms, are used to mine images. Image mining is a relatively new area of research. It is used to extract patterns from multidated satellite images of a geographic area.

scholarly journals Efficient land use based on remote sensing data

2021 ◽  
Vol 3 ◽  
pp. 180-185
Author(s):  
Y. M. Kenzhegaliyev ◽  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Crop Productivity ◽  
The State ◽  
Agricultural Crops ◽  
Earth Remote Sensing ◽  
Spectral Reflectivity ◽  
Sensing Data ◽  
Current Location

The goal -is to explore ways of using Earth remote sensing data for efficient land use. Methods - detailed information on current location of certain types of agricultural crops in the study areas has been summarized, which opens up opportunities for the effective use of cultivated areas. It was revealed that the basis of the principle of the method under consideration is the relationship between the state and structure of vegetation types with its reflective ability. It has been determined that information on the spectral reflective property of the vegetation cover in the future can help replace more laborious methods of laboratory analysis. For classification of farmland, satellite images of medium spatial resolution with a combination of channels in natural colors were selected. Results - a method for identifying agricultural plants by classification according to the maximum likelihood algorithm was considered. The commonly used complexes of geoinformation software products with modules for special image processing allow displaying indicators in the form of raster images. It is shown that the use of Earth remote sensing data is the most relevant solution in the field of crop recognition and makes it possible to simplify the implementation of such types of work as the analysis of the intensity of land use, the assessment of the degree of pollution with weeds and determination of crop productivity. Conclusions - the research results given in the article indicate that timely information on the current location of certain types of agricultural crops in the studied territories significantly simplifies the implementation of the tasks and increases the resource potential of agricultural lands. In turn, the timing of the survey and the state of environment affect the spectral reflectivity of vegetation.

scholarly journals Monitoring of glaciers in the Polar Urals using remote sensing Data

2020 ◽  
Vol 217 ◽  
pp. 04003
Author(s):  
Natalia Martynova ◽  
Valentina Budarova ◽  
Artem Sheremetinsky ◽  
Nikita Mezentsev
Keyword(s):  
Remote Sensing ◽  
Technological Progress ◽  
Satellite Images ◽  
Research Question ◽  
Remote Sensing Data ◽  
Field Work ◽  
Sources Of Information ◽  
Sensing Data ◽  
Polar Urals ◽  
The Polar Urals

The development of technological progress provides more opportunities for indirect monitoring of changes in the environment. Remote sensing is one of The most accessible and reliable sources of information. In this work, we used satellite images from the Landsat family. The theoretical justification of the research question is given. The research methodology was developed. Collection and processing of satellite images for various time periods. A series of schematic maps based on remote sensing Data has been created. As a result of digitization of satellite images, 9 glacier contours were obtained by year. We determined the area of the Romantics glacier and found that it lost at least 60% of its original area. These studies were used to build a series of cartographic schemes that clearly show the reduction of the glacier area. It is concluded that the use of remote sensing allows you to solve problems, monitoring the object. The use of this method allows not only to save time for field work, but also material costs for expedition equipment and various equipment. This method can be tested on any objects.

scholarly journals Self-Organizing B2B Enterprise of Distributed Photogrammetric Processing of Terrain Images in ETRIS

2020 ◽  
pp. 155-179
Author(s):  
Oleg Karsaev ◽  
Igor Shuklin ◽  
Sergey Yushchenko
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Distributed Information System ◽  
Distributed Information ◽  
Sensing Data ◽  
Geographically Distributed ◽  
Software Information ◽  
Self Organizing

An approach to the dynamic formation (adjustment) of schedules for distributed photogrammetric image processing in a network of ground centers included in the United geographically distributed information system for receiving and processing Earth remote sensing data from space is considered. Having the fullest satisfaction of requirements of consumers to the satellite images of necessary areas, the approach provides the formation of self-organizing B2B enterprises in the specified network providing information, software and hardware resources of the ground-based facilities of various departmental and other accessories for photogrammetric processing of any received images of the area from the the United geographically distributed information system. It is shown, that a search in B2B enterprise nodes and borrowing the required resources will allow ground centers to flexibly scale physical and virtual means of photogrammetric processing of Earth remote sensing data, quickly form their local structural and functional organizations depending on the current properties of the consumer requests flow for receiving Earth remote sensing data in the United geographically distributed information system, characteristics of the flow of terrain survey materials from orbital monitoring tools, and also take into account the visual and measuring properties of images of the area subject to photogrammetric processing. A method for truncating the set of potential performers of the application in accordance with the existing semantic and other restrictions on the composition of the desired set of performers is proposed. Also mechanisms to encourage ground centers to provide idle resources to B2B enterprise nodes are proposed. They are based on the possibility of receiving monetary or other remuneration from a ground center for participating in distributed application servicing. The development of a well-known model of a self-organizing B2B enterprise creates conditions for a more efficient organization of servicing the flow of applications in the United geographically distributed information system by attracting unused software, information and hardware resources of ground centers of various departmental affiliations.

scholarly journals Recognition of “flowering” processes on the base of remote sensing data in shallow water ponds on the example of the Azov Sea

2020 ◽  
Vol 175 ◽  
pp. 12013 ◽  
Author(s):  
Marina Ganzhur ◽  
Nikita Dyachenko ◽  
Olga Smirnova ◽  
Anna Poluyan ◽  
Natalya Panasenko
Keyword(s):  
Remote Sensing ◽  
Shallow Water ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Shallow Waters ◽  
Sea Of Azov ◽  
The Sea Of Azov ◽  
Sensing Data ◽  
Azov Sea

This work considers to the processes of «bloom» phytoplankton processes that cause hypoxic phenomena in shallow waters the example of the Sea of Azov. For the accumulation of information, multichannel satellite images of remote sensing are taken as a basis. In the process, the task of programmatically highlighting the contours of the areas of «bloom» is implemented.

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