scholarly journals Sampling strategy of an epidemiological survey using a satellite image program

2019 ◽  
Vol 53 ◽  
pp. 47 ◽  
Author(s):  
Ticiane De Góes Mário Ferreira ◽  
José Mariano Da Rocha ◽  
Silvia Cardoso de David ◽  
Jociana Boligon ◽  
Maísa Casarin ◽  
...  
Keyword(s):  
Life Stress ◽  
Skin Color ◽  
Satellite Images ◽  
Periodontal Diseases ◽  
Satellite Image ◽  
Sampling Strategy ◽  
Epidemiological Survey ◽  
Clinical Approach ◽  
Color Data

OBJECTIVE: To describe the sampling strategy of an epidemiological survey with the aid of satellite images, including details of the multistage probability sampling process. METHODS: A probability sample of individuals living in the rural area of Rosário do Sul, state of Rio Grande do Sul, Brazil, aged 15 years old or more, was evaluated. Participants answered questionnaires (medical history, sociodemographic characteristics, habits, alcohol use, quality of life, stress, rumination, and self-perceived periodontal diseases), and were subjected to clinical oral examinations as well as anthropometric measurements (blood pressure, height, weight, abdominal and waist circumferences). Oral evaluation comprehended a complete periodontal exam at six sites per tooth, including the following assessments: furcation involvement; dental abrasion; tooth decay, including the indexing of missing and filled surfaces; O’Brien index; gingival abrasion; oral cavity and lip lesions; complete periapical radiographic exam, and use of prostheses. Besides this oral clinical approach, subgingival plaque, crevicular gingival fluid, saliva, and blood samples were collected. Examiners were trained and calibrated during previous evaluations. A pilot study allowed the logistic of the performed exams to be adjusted as needed. RESULTS: Among 1,087 eligible individuals, 688 were examined (63.3%). Age, sex, and skin color data were compared to data from the last demographic census (2010) of the Brazilian Institute of Geography and Statistics, which served to validate the sampling strategy. CONCLUSIONS: The careful methods used in this study, in which satellite images were used in the delimitation of epidemiological areas, ensure the quality of the estimates obtained and allow for these estimates to be used in oral health surveillance and health policies improvements.

scholarly journals An Investigation on Water Quality of Darlik Dam Drinking Water using Satellite Images

2010 ◽  
Vol 10 ◽  
pp. 1293-1306 ◽  
Author(s):  
Erhan Alparslan ◽  
H. Gonca Coskun ◽  
Ugur Alganci
Keyword(s):  
Water Quality ◽  
Satellite Images ◽  
Satellite Image ◽  
Water Reservoir ◽  
Acquisition Time ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Dissolved Matter ◽  
Image Acquisition Time

Darlik Dam supplies 15% of the water demand of Istanbul Metropolitan City of Turkey. Water quality (WQ) in the Darlik Dam was investigated from Landsat 5 TM satellite images of the years 2004, 2005, and 2006 in order to determine land use/land cover changes in the watershed of the dam that may deteriorate its WQ. The images were geometrically and atmospherically corrected for WQ analysis. Next, an investigation was made by multiple regression analysis between the unitless planetary reflectance values of the first four bands of the June 2005 Landsat TM image of the dam and WQ parameters, such as chlorophyll-a, total dissolved matter, turbidity, total phosphorous, and total nitrogen, measured at satellite image acquisition time at seven stations in the dam. Finally, WQ in the dam was studied from satellite images of the years 2004, 2005, and 2006 by pattern recognition techniques in order to determine possible water pollution in the dam. This study was compared to a previous study done by the authors in the Küçükçekmece water reservoir, also in Istanbul City.

scholarly journals GEOMETRIC QUALITY EVALUATION METHOD FOR MULTI-VIEW SATELLITE IMAGES IN 3D RECONSTRUCTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 533-539
Author(s):  
H. Yi ◽  
X. Chen ◽  
D. Wang ◽  
S. Du ◽  
B. Xu ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Satellite Images ◽  
Evaluation Method ◽  
Satellite Image ◽  
Geometric Distortion ◽  
Geometric Quality ◽  
Deviation Coefficient ◽  
Ground Grid ◽  
Straight Lines

Abstract. The quality of the 3D model reconstructed using multi-view satellite image depends on the quality of the image. To evaluate the geometric quality of the satellite image, we proposed a method to evaluate the geometric distortion for satellite images and defined the deviation coefficient as a metric to evaluate the bending degree of a curve. After projecting a ground grid consisting of straight lines into the image space, the geometric distortion of the image can be evaluated quantitatively by calculating the deviation coefficients of the projection trajectories. Experiments have been carried out with three datasets obtained by JiLin-1, GaoFen-2, and WorldView-3 respectively. The results show that the proposed method can used to evaluate the geometric quality of satellite images effectively, and this evaluation method will be useful in image selecting in 3D reconstruction using multi-view satellite images.

Vector Quantization for Satellite Image Compression

2016 ◽  
Vol 5 ◽  
pp. 22
Author(s):  
Sanjith Sathya Joseph ◽  
R. Ganesan
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Signal To Noise ◽  
Transmission Process ◽  
Noise Ratio ◽  
Time Required

Image compression is the process of reducing the size of a file without humiliating the quality of the image to an unacceptable level by Human Visual System. The reduction in file size allows as to store more data in less memory and speed up the transmission process in low bandwidth also, in case of satellite images it reduces the time required for the image to reach the ground station. In order to increase the transmission process compression plays an important role in remote sensing images.  This paper presents a coding scheme for satellite images using Vector Quantization. And it is a well-known technique for signal compression, and it is also the generalization of the scalar quantization.  The given satellite image is compressed using VCDemo software by creating codebooks for vector quantization and the quality of the compressed and decompressed image is compared by the Mean Square Error, Signal to Noise Ratio, Peak Signal to Noise Ratio values.

scholarly journals Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

2019 ◽  
Vol 224 ◽  
pp. 04010
Author(s):  
Viacheslav Voronin
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Satellite Images ◽  
Color Image ◽  
State Of The Art ◽  
Satellite Image ◽  
Detailed Model ◽  
Global Contrast ◽  
Enhancement Algorithm

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.

A Comparative Analysis of Existing Satellite Image Enhancement Techniques for Effective Visual Display

2019 ◽  
Vol 16 (9) ◽  
pp. 4003-4007 ◽  
Author(s):  
Neetu Manocha ◽  
Rajeev Gupta
Keyword(s):  
Comparative Analysis ◽  
Image Enhancement ◽  
High Frequency ◽  
Satellite Images ◽  
Satellite Image ◽  
Research Work ◽  
Visual Display ◽  
Image Resolution ◽  
High Image Resolution

Due to environment untidiness and inappropriate setting or dealing of camera, a satellite image contains blur or other types of noises. These images are captured by satellites consist lots of information about the surface of earth or other planets. But, due to blur or noise, the quality of these images is degraded. Now days, there are many fields in which satellite images are used, which effects the environment. The accuracy and effective visual display of satellite images with high image resolution using CBIR technique is major concern. This paper presents a comparative analysis of existing satellite image enhancement techniques to reduce the blur of an image on the basis of accuracy and response time. The aim of research work is to eliminate the noise without losing high frequency details and to enhance the image for effective visual display.

scholarly journals Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

2020 ◽  
Vol 12 (24) ◽  
pp. 4152
Author(s):  
Giruta Kazakeviciute-Januskeviciene ◽  
Edgaras Janusonis ◽  
Romualdas Bausys ◽  
Tadas Limba ◽  
Mindaugas Kiskis
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Quality Assessment ◽  
Satellite Images ◽  
Clustering Algorithm ◽  
Satellite Image ◽  
Quality Metrics ◽  
Data Set ◽  
Segmentation Quality

The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation.

scholarly journals A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using Adaptive Image Quality Modification

2021 ◽  
Vol 13 (16) ◽  
pp. 3301
Author(s):  
Yeonju Choi ◽  
Sanghyuck Han ◽  
Yongwoo Kim
Keyword(s):  
Image Quality ◽  
Satellite Images ◽  
Satellite Image ◽  
Super Resolution ◽  
Ground Truth ◽  
Perceptual Quality ◽  
High Quality ◽  
Degradation Model ◽  
Sr Method

In recent years, research on increasing the spatial resolution and enhancing the quality of satellite images using the deep learning-based super-resolution (SR) method has been actively conducted. In a remote sensing field, conventional SR methods required high-quality satellite images as the ground truth. However, in most cases, high-quality satellite images are difficult to acquire because many image distortions occur owing to various imaging conditions. To address this problem, we propose an adaptive image quality modification method to improve SR image quality for the KOrea Multi-Purpose Satellite-3 (KOMPSAT-3). The KOMPSAT-3 is a high performance optical satellite, which provides 0.7-m ground sampling distance (GSD) panchromatic and 2.8-m GSD multi-spectral images for various applications. We proposed an SR method with a scale factor of 2 for the panchromatic and pan-sharpened images of KOMPSAT-3. The proposed SR method presents a degradation model that generates a low-quality image for training, and a method for improving the quality of the raw satellite image. The proposed degradation model for low-resolution input image generation is based on Gaussian noise and blur kernel. In addition, top-hat and bottom-hat transformation is applied to the original satellite image to generate an enhanced satellite image with improved edge sharpness or image clarity. Using this enhanced satellite image as the ground truth, an SR network is then trained. The performance of the proposed method was evaluated by comparing it with other SR methods in multiple ways, such as edge extraction, visual inspection, qualitative analysis, and the performance of object detection. Experimental results show that the proposed SR method achieves improved reconstruction results and perceptual quality compared to conventional SR methods.

scholarly journals A Survey on Intensification Techniques used in Satellite Images

2021 ◽  
Vol 9 (VII) ◽  
pp. 2242-2245
Author(s):  
Bipin D. Tamkhane
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Research Work ◽  
Research Area ◽  
Research Areas ◽  
Image Intensification ◽  
Human Eyes

An image intensification is a required methodology in field of Satellite image research area. The images taken through satellite are captured from very longer distance and because of this images having garbling and noise as lots of airy barriers are present in between the path. The usage of Satellite images is very diverse in research areas like astrological studies, geographical studies, study of geoscience, etc. Nowadays, after taking a snapshot of an image, some of the radiometric or geometric based enhancement techniques are applied on the images taken from satellite but these techniques do not fulfill the requirements in all application areas. This is what there is a need to improvise the quality of an image before it is being actually used. The main objective behind this research work is to understand the different methodologies used in intensification of satellite images and how can we perform more improvements to existing techniques so these type of images which are taken from satellite are intelligible to the human eyes. The meaning of intensification in term of image is nothing but the altering of a look and feel of the image in a way that the information contained by that image is more readily intelligible visually.

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

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