Remote sensing measurement of forest parameters in the insular part of South-East Asia : high/low resolution image data analysis

2002 ◽  
Author(s):  
C. Estreguil ◽  
G. D'Souza
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
East Asia ◽  
Image Data ◽  
South East Asia ◽  
Low Resolution ◽  
Resolution Image

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 Improved Sparse Representation Super-Resolution algorithm for Remote Sensing Image

2018 ◽  
Vol 232 ◽  
pp. 02040
Author(s):  
Fuzhen Zhu ◽  
Xin Huang ◽  
Yue Liu ◽  
Haitao Zhu
Keyword(s):  
Remote Sensing ◽  
Sparse Representation ◽  
Super Resolution ◽  
Objective Evaluation ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
Resolution Image ◽  
Training Images ◽  
Feature Extract ◽  
Resolution Algorithm

In order to obtain higher quality super-resolution reconstruction (SRR) of remote sensing images, an improved sparse representation remote sensing images SRR method is proposed in this paper. First, low-resolution image is processed by improved feature extract operator. The high-resolution image and low-resolution image blocks have the same sparse representation coefficient, so the SRR image with higher spatial resolution can be derived from the sparse representation coefficients which have been obtained from low-resolution image. The improved feature extraction operator is a method to get more detail and texture information from the training images. Experiment results show that more texture details can be obtained in the result of SRR remote sensing images subjectively. At the same time, the objective evaluation parameters are improved greatly. The peak PSNR is increased about 2.50dB and 0.50 dB, RMSE is decreased about 2.80 and 0.3 compared with bicubic interpolation algorithm and Ref[8] algorithm respectively.

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.

scholarly journals Low Resolution Face Recognition Using Generative Adversarial Network (GAN)

2020 ◽  
Author(s):  
Howard Martin ◽  
Suharjito
Keyword(s):  
Face Recognition ◽  
Super Resolution ◽  
Image Data ◽  
Face Image ◽  
Image Resolution ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
Surveillance Camera ◽  
Adversarial Network

Abstract Face recognition has a lot of use on smartphone authentication, finding people, etc. Nowadays, face recognition with a constrained environment has achieved very good performance on accuracy. However, the accuracy of existing face recognition methods will gradually decrease when using a dataset with an unconstrained environment. Face image with an unconstrained environment is usually taken from a surveillance camera. In general, surveillance cameras will be placed on the corner of a room or even on the street. So, the image resolution will be low. Low-resolution image will cause the face very hard to be recognized and the accuracy will eventually decrease. That is the main reason why increasing the accuracy of the Low-Resolution Face Recognition (LRFR) problem is still challenging. This research aimed to solve the Low-Resolution Face Recognition (LRFR) problem. The datasets are YouTube Faces Database (YTF) and Labelled Faces in The Wild (LFW). In this research, face image resolution would be decreased using bicubic linear and became the low-resolution image data. Then super resolution methods as the preprocessing step would increase the image resolution. Super resolution methods used in this research are Super resolution GAN (SRGAN) [1] and Enhanced Super resolution GAN (ESRGAN) [2]. These methods would be compared to reach a better accuracy on solving LRFR problem. After increased the image resolution, the image would be recognized using FaceNet. This research concluded that using super resolution as the preprocessing step for LRFR problem has achieved a higher accuracy compared to [3]. The highest accuracy achieved by using ESRGAN as the preprocessing and FaceNet for face recognition with accuracy of 98.96 % and Validation rate 96.757 %.

scholarly journals Design of Remote Sensing Image Data Analysis and Processing Platform Based on Environmental Monitoring

2021 ◽  
Vol 2136 (1) ◽  
pp. 012056
Author(s):  
Yang Tang ◽  
Jiongchao Yan ◽  
Yueqi Wu ◽  
Jie Hong ◽  
Lei Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Environmental Monitoring ◽  
Image Data ◽  
Remote Sensing Image ◽  
Modern Technology ◽  
Positive Role ◽  
Continuous Innovation ◽  
Detection Technology ◽  
Processing Platform

Abstract In the continuous innovation of modern technology concept, remote sensing technology as an advanced and practical comprehensive detection technology has been widely used in many fields. Especially for environmental monitoring, the rational use of remote sensing image data analysis and processing platform can not only obtain valuable environmental information, but also provide effective management decisions for climate changeable natural disasters and other issues. Therefore, on the basis of understanding the design scheme of remote sensing image data analysis and processing platform system, this paper makes clear the positive role of remote sensing image processing technology in the development of environmental monitoring based on the application of the platform.

scholarly journals Super-resolution techniques in aerial images for remote sensing

2019 ◽  
Author(s):  
Victor Carneiro Lima ◽  
Renato da Rocha Lopes
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Iterative Process ◽  
Agricultural Research ◽  
Super Resolution ◽  
Aerial Images ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
Extract Information

Super-resolution algorithms, specially when applied in remote sensing, are widely used for many purposes as defense and agricultural research. Classical super-resolution algorithms use multiple low-resolution (LR) images of the target to extract information and use them to build a new image of superior resolution. The LR sources must differ in the sub-pixel range. In contrast, this paper applies an iterative process, using a single LR image to produce a high resolution image.

scholarly journals Sensitivity of WRF-Chem model resolution in simulating particulate matter in South-East Asia

2020 ◽  
Author(s):  
Adedayo Rasak Adedeji ◽  
Lalit Dagar ◽  
Mohammad Iskandar Petra ◽  
Liyanage C. De Silva ◽  
Zhining Tao
Keyword(s):  
Particulate Matter ◽  
High Resolution ◽  
East Asia ◽  
Correlation Coefficient ◽  
Biomass Burning ◽  
Horizontal Resolution ◽  
South East Asia ◽  
Low Resolution ◽  
Pm10 And Pm2.5 ◽  
Resolution Simulation

Abstract. Frequent haze episodes commonly caused by biomass burning has been a concern in South East Asia. One of such events was the June 2013 severe haze in the region. This study assessed the ability of WRF-Chem in capturing the spatial variability and concentrations of particulate emissions during this period. It analyzed the regional biomass burning emissions and its transport leading to higher particulate matter levels in the region. In order to analyze the effect of grid-scale, the horizontal resolution of the simulation was varied between low-resolution (100 km) and high-resolution (20 km). Evaluations of the simulations were made against meteorological observations pertinent to emission and transport of particulate matter, including surface and vertical air profile variables such as temperature, relative humidity, and wind speed and direction. Particulate matter (PM10 and PM2.5) levels were evaluated using ground measurements in Brunei and Singapore respectively. The meteorological parameters were adequately represented across the model simulations. Increasing the horizontal resolution of the simulations generally improved the representation of meteorology and air quality but some prognostic variables maintained similar or better performance with coarse resolution simulation. With the high-resolution simulation, PM10 concentration in Brunei had a correlation coefficient around 0.4, and the simulated PM2.5 level in Singapore had correlation coefficient around 0.9. Whereas, the low-resolution simulation had correlation coefficients around 0.2 and 0.8 for PM10 and PM2.5 levels at Brunei and Singapore, respectively. Both simulations could not repeat aerosol optical depth (AOD) from reanalysis unless the biomass burning emissions were enhanced. An enhancement factor of 6 with high-resolution simulation gave PM10 and PM2.5 correlations around 0.6 and 0.9 in Brunei and Singapore respectively.

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