scholarly journals Mangrove Distribution in Riau Islands Using Remote Sensing Technology

2017 ◽  
Vol 1 (2) ◽  
pp. 58-62 ◽  
Author(s):  
Sudra Irawan ◽  
Dwi Ely Kurniawan ◽  
Wenang Anurogo ◽  
Muhammad Zainuddin Lubis
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Data ◽  
Research Data ◽  
Resolution Image ◽  
Mangrove Area ◽  
High Resolution Image ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Data Application

Mangrove mapping is done with remote sensing technology using high-resolution image data. Application and information are then presented in web form. This study aims to map the mangrove distribution in Riau Islands, Indonesia. Based on the analysis, from the research data obtained the total area of mangrove in Riau Islands in 2011 and 2017 amounted to 71,504.83 Ha and 64,218.90 Ha, decreased by 7,285, 93 Ha or decreased by 10.19%. Based on the regency, the largest mangrove area in 2017 is located in Batam City of 22,964.77 Ha, then Karimun Regency (13,659,58 Ha), Lingga Regency (11,881.61 Ha), Regency of Bintan (9,701.49) Ha, Natuna Regency (2,477.16 Ha), Tanjungpinang City (1,847.65 Ha), and Anambas Regency (1,686.61 Ha). The magnitude of the widespread change (widespread reduction) occurring over the years between 2011 and 2017 by district, Natuna Regency experienced the largest reduction of 1,949.69 Ha or around 41.39%, followed by Lingga Regency of 1,947.15 Ha (14.08%), Tanjungpinang Municipality of 284.13 Ha (13.33%), Karimun Regency 1,920.93 Ha (12.33%), Anambas Regency of 195.90 Ha (10.40%), Batam City 1,094.83 Ha (4.55%) and Bintan Regency with 93.29 Ha (0, 95%). Opportunities that the pixels classified on the mangrove image are truly mangrove on the facts in the field.

scholarly journals THE TYPHOON DISASTER ANALYSIS EMERGENCY RESPONSE SYSTEM BASED ON UAV REMOTE SENSING TECHNOLOGY

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 959-965
Author(s):  
Q. J. Chen ◽  
Y. R. He ◽  
T. T. He ◽  
W. J. Fu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Analysis ◽  
Satellite Image ◽  
Image Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Disaster Information ◽  
Satellite Image Data ◽  
Typhoon Disaster

Abstract. The satellite image data has some shortcomings such as poor timeless, incomplete disaster information and so on in the typhoon disaster analysis. Compared with the satellite image data, unmanned aerial vehicle (UAV) remote sensing technology has the characteristics of flexibility, convenience, high resolution and so on. It plays a great role in the aspect of obtaining the images and systematically analyze the disaster data. This research based on UAV technology to obtain the high resolution image data and complied the disaster thematic maps after interpretation, as well as determining the data model. Subsequently, determining the system used Html, Javascript and CSS to build the system framework. Combining with Postgre SQL database, Leaflet map module and Echarts diagram and other technologies to perform the feasibility analysis and the detailed design of the integrated system. Finally, it could accurately and comprehensively obtain the system’s disaster monitoring, the typhoon track display, the diagram statistics and visual analysis of the data processing, as well it could deeply analysis and management for the disaster information and assessment. The application shows that this system could provide the information support for future emergency rescue, which is of great significance for the monitoring and preventing the occurrence natural disasters in the future.

Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets

2018 ◽  
Vol 15 (9) ◽  
pp. 1451-1455 ◽  
Author(s):  
Grant J. Scott ◽  
Kyle C. Hagan ◽  
Richard A. Marcum ◽  
James Alex Hurt ◽  
Derek T. Anderson ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Deep Neural Networks ◽  
Image Data ◽  
Data Sets ◽  
Resolution Image ◽  
High Resolution Image

Laser radar cross-section estimation from high-resolution image data

1992 ◽  
Vol 31 (14) ◽  
pp. 2452 ◽  
Author(s):  
G. R. Osche ◽  
K. N. Seeber ◽  
Y. F. Lok ◽  
D. S. Young
Keyword(s):  
High Resolution ◽  
Cross Section ◽  
Image Data ◽  
Radar Cross Section ◽  
Laser Radar ◽  
Resolution Image ◽  
High Resolution Image

scholarly journals Optimal Allocation Algorithm of Geological and Ecological High-resolution Remote Sensing Monitoring Sampling Points

2020 ◽  
Vol 24 (1) ◽  
pp. 105-110
Author(s):  
Taifu Bi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Search Algorithm ◽  
Support Vector ◽  
Allocation Algorithm ◽  
Remote Sensing Monitoring ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Sampling Points ◽  
Selective Search

Abstract: The purpose of this study is to solve the problem of unsatisfactory image representation of monitoring sampling points in high-resolution remote sensing due to the complexity of geological ecology. Firstly, three algorithms used in remote sensing technology were introduced, that is, extraction algorithm of monitoring sampling point (selective search algorithm), discriminant algorithm (support vector machine) and BING algorithm. Then, the BING algorithm was improved. Finally, the superiority of the improved BING algorithm was verified through experimental data set. The results showed that selective search algorithm could generate more candidate windows in remote sensing image and had better adaptability. The improved algorithm had higher quality of candidate windows extracted from remote sensing images. Although the IBING algorithm could greatly improve the extraction speed of remote sensing, the detection time of each image became larger. Such testing times were still acceptable. Therefore, in this research, the allocation algorithm of geological and ecological high-resolution remote sensing monitoring sampling points was optimized, which had a good guiding significance for the application of remote sensing technology in geological and ecological research.

THE RECONSTRUCTION OF SATELLITE IMAGES BASED ON FRACTAL INTERPOLATION

Fractals ◽  
2011 ◽  
Vol 19 (03) ◽  
pp. 347-354 ◽  
Author(s):  
CHING-JU CHEN ◽  
SHU-CHEN CHENG ◽  
Y. M. HUANG
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Image Resolution ◽  
Source Image ◽  
Fractal Interpolation ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image

This study discussed the application of a fractal interpolation method in satellite image data reconstruction. It used low-resolution images as the source data for fractal interpolation reconstruction. Using this approach, a high-resolution image can be reconstructed when there is only a low-resolution source image available. The results showed that the high-resolution image data from fractal interpolation can effectively enhance the sharpness of the border contours. Implementing fractal interpolation on an insufficient image resolution image can avoid jagged edges and mosaic when enlarging the image, as well as improve the visibility of object features in the region of interest. The proposed approach can thus be a useful tool in land classification by satellite images.

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