Thailand has implemented GNSS Continuously Operating Reference Stations (CORS) and Network Real-Time Kinematic (NRTK) Positioning services collaborated by several government agencies and universities for various applications. This research includes 114 GNSS CORSs; providing 143 triangle loops and covering 63 provinces nationwide, to determine and analyse the obtained horizontal positioning accuracy and their ground station geometries. Triangulation networks are categorised in terms of shapes and baseline distances. Well- and ill-conditioned triangles are defined in accordance with different baseline lengths. The horizontal positioning accuracy is computed using static and NRTK positioning determinations whilst static positioning results are defined as the ground truth. Student's T tests are used to ensure the significance of calculated results according to each test case. The results show obtained horizontal positioning accuracies are within the specified accuracy of less than 4 centimetres and do not show any significant differences based on the defined significant level of 0.05. This GNSS CORS triangulation network geometry scheme does not influence the computed horizontal positioning accuracy obtained from NRTK-Virtual Reference Station (VRS) GNSS positioning services; however, the positioning accuracy is still directly due to distances between nearest GNSS CORS or its triangulation network, therefore, further suggestions are provided.
{"title":"Geometric and Statistical Assessments on Horizontal Positioning Accuracy in Relation with GNSS CORS Triangulations of NRTK Positioning Services in Thailand","authors":"","doi":"10.52939/ijg.v19i2.2559","DOIUrl":"https://doi.org/10.52939/ijg.v19i2.2559","url":null,"abstract":"Thailand has implemented GNSS Continuously Operating Reference Stations (CORS) and Network Real-Time Kinematic (NRTK) Positioning services collaborated by several government agencies and universities for various applications. This research includes 114 GNSS CORSs; providing 143 triangle loops and covering 63 provinces nationwide, to determine and analyse the obtained horizontal positioning accuracy and their ground station geometries. Triangulation networks are categorised in terms of shapes and baseline distances. Well- and ill-conditioned triangles are defined in accordance with different baseline lengths. The horizontal positioning accuracy is computed using static and NRTK positioning determinations whilst static positioning results are defined as the ground truth. Student's T tests are used to ensure the significance of calculated results according to each test case. The results show obtained horizontal positioning accuracies are within the specified accuracy of less than 4 centimetres and do not show any significant differences based on the defined significant level of 0.05. This GNSS CORS triangulation network geometry scheme does not influence the computed horizontal positioning accuracy obtained from NRTK-Virtual Reference Station (VRS) GNSS positioning services; however, the positioning accuracy is still directly due to distances between nearest GNSS CORS or its triangulation network, therefore, further suggestions are provided.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42793948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this study was to determine the relationship between the proportion of forest area, soil moisture index, and net primary productivity in the Pa Sak Ngam, Luang Nuea Subdistrict, Doi Saket District Chiang Mai, Thailand. The investigation was conducted during dry season in 2009 and 2019 utilizing systematic sampling inside a 500 m × 500 m image grid to measure these factors. Landsat 5 TM and Landsat 8 OLI/TIRS satellite images were classified using the Random Forest to obtain the proportion of forest area. Soil moisture was calculated using the soil moisture index obtained from land surface temperature and the normalized difference vegetation index. The Physiological Processes Predicting Growth (3-PGs) model was used to compute net primary productivity. In 2009, the analysis revealed a moderately strong positive correlation between the proportion of forest area and both soil moisture and net primary productivity. In contrast, in 2019, a weak positive association was found between low forest cover percentage and both soil moisture and net primary productivity. A comparison of the results from the two time periods indicated that the association between the three variables was stronger in 2009 than in 2019. This may be attributed to the increase in average forest cover from 85.583% to 92.349% over the two time periods. Effective management of forest restoration and expansion can enhance the water cycle and increase the flow of energy and productivity.
{"title":"Assessing the Relationship between Forest Proportion, Soil Moisture Index and Net Primary Productivity in Pa Sak Ngam, Chiang Mai Province, Thailand","authors":"","doi":"10.52939/ijg.v19i2.2563","DOIUrl":"https://doi.org/10.52939/ijg.v19i2.2563","url":null,"abstract":"The objective of this study was to determine the relationship between the proportion of forest area, soil moisture index, and net primary productivity in the Pa Sak Ngam, Luang Nuea Subdistrict, Doi Saket District Chiang Mai, Thailand. The investigation was conducted during dry season in 2009 and 2019 utilizing systematic sampling inside a 500 m × 500 m image grid to measure these factors. Landsat 5 TM and Landsat 8 OLI/TIRS satellite images were classified using the Random Forest to obtain the proportion of forest area. Soil moisture was calculated using the soil moisture index obtained from land surface temperature and the normalized difference vegetation index. The Physiological Processes Predicting Growth (3-PGs) model was used to compute net primary productivity. In 2009, the analysis revealed a moderately strong positive correlation between the proportion of forest area and both soil moisture and net primary productivity. In contrast, in 2019, a weak positive association was found between low forest cover percentage and both soil moisture and net primary productivity. A comparison of the results from the two time periods indicated that the association between the three variables was stronger in 2009 than in 2019. This may be attributed to the increase in average forest cover from 85.583% to 92.349% over the two time periods. Effective management of forest restoration and expansion can enhance the water cycle and increase the flow of energy and productivity.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43134169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aims at integrating GIS method and fuzzy AHP to evaluate the impact of climate change under the vulnerability concept. The results of this empirical study in Da Nang city have significant scientific contribution to the generation of comprehensive indicators for assessing the vulnerability in coastal cities in Central region of Vietnam. The approach of the Intergovernmental Panel on Climate Change (IPCC) in climate change vulnerability assessment has been examined considering three main components of vulnerability which are exposure to hazards, local sensitivity and adaptive capacity. GIS-based approach was applied to generate a set of indicators and the fuzzy AHP method was investigated for determination of the weighted scheme for parameters included in the climate vulnerability assessment. The study results indicate that the coastal districts including Son Tra and Ngu Hanh Son districts are most vulnerable to climate change due to high exposure, high sensitivity and limited adaptive capacity. On the contrary, the district with high level of adaptive capacity such as Hai Chau district is usually ranged in low level of vulnerability. The results confirm the importance of enhancing adaptive capacity in responding to the impact of climate change.
{"title":"Climate Change Vulnerability Assessment Using GIS and Fuzzy AHP on an Indicator-Based Approach","authors":"","doi":"10.52939/ijg.v19i2.2565","DOIUrl":"https://doi.org/10.52939/ijg.v19i2.2565","url":null,"abstract":"This study aims at integrating GIS method and fuzzy AHP to evaluate the impact of climate change under the vulnerability concept. The results of this empirical study in Da Nang city have significant scientific contribution to the generation of comprehensive indicators for assessing the vulnerability in coastal cities in Central region of Vietnam. The approach of the Intergovernmental Panel on Climate Change (IPCC) in climate change vulnerability assessment has been examined considering three main components of vulnerability which are exposure to hazards, local sensitivity and adaptive capacity. GIS-based approach was applied to generate a set of indicators and the fuzzy AHP method was investigated for determination of the weighted scheme for parameters included in the climate vulnerability assessment. The study results indicate that the coastal districts including Son Tra and Ngu Hanh Son districts are most vulnerable to climate change due to high exposure, high sensitivity and limited adaptive capacity. On the contrary, the district with high level of adaptive capacity such as Hai Chau district is usually ranged in low level of vulnerability. The results confirm the importance of enhancing adaptive capacity in responding to the impact of climate change.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45893077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rice (Oryza sativa L.) is a staple food for more than half of the global population. This research, therefore, aims to explore the estimation of crop yields towards the application of unmanned aerial vehicles (UAVs). The research areas are the sample rice fields owned by Sam Ngam Large-Scale Rice Production Community Enterprise in Don Tum District, Nakhon Pathom. The data collected by both RGB and multispectral UAVs was used for estimating the crop yields of Rice Department 41 (RD41), a rice variety, and then analyzed by a geographic information system (GIS). Multiple Linear Regression was applied to factor analysis for the purpose of crop yield estimation based on the factors investigated and obtained by the UAVs. These factors included vegetation indexes (i.e. Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, and Triangular Greenness Index), plant height, and canopy coverage. The prediction of the analysis model was proved to be valid (R2 = 0.99; RMSE = 2.506 g.). Extreme Gradient Boosting (XGBoost) was applied to increase the accuracy of the estimation (RMSE = 0.557 g.; MAE = 0.364). The findings of study showed that the utilization of UAVs could contribute to the estimation of crop yield in the research areas.
水稻（Oryza sativa L.）是全球一半以上人口的主食。因此，本研究旨在探索无人机应用中的作物产量估算。研究区域是位于Nakhon Pathom Don Tum区的Sam Ngam大型水稻生产社区企业拥有的样本稻田。RGB和多光谱无人机收集的数据用于估计水稻品种41号水稻部门（RD41）的作物产量，然后通过地理信息系统（GIS）进行分析。基于无人机调查和获得的因素，将多元线性回归应用于因素分析，以估计作物产量。这些因素包括植被指数（即归一化植被指数、绿色归一化植被指数和三角绿色指数）、植物高度和冠层覆盖率。分析模型的预测被证明是有效的（R2=0.99；RMSE=2.506g）。应用极限梯度助推（XGBoost）来提高估计的准确性（RMSE=0.557g；MAE=0.364）。研究结果表明，无人机的使用有助于估计研究区的作物产量。
{"title":"The Application of Unmanned Aerial Vehicles (UAVs) and Extreme Gradient Boosting (XGBoost) to Crop Yield Estimation: A Case Study of Don Tum District, Nakhon Pathom, Thailand","authors":"","doi":"10.52939/ijg.v19i2.2569","DOIUrl":"https://doi.org/10.52939/ijg.v19i2.2569","url":null,"abstract":"Rice (Oryza sativa L.) is a staple food for more than half of the global population. This research, therefore, aims to explore the estimation of crop yields towards the application of unmanned aerial vehicles (UAVs). The research areas are the sample rice fields owned by Sam Ngam Large-Scale Rice Production Community Enterprise in Don Tum District, Nakhon Pathom. The data collected by both RGB and multispectral UAVs was used for estimating the crop yields of Rice Department 41 (RD41), a rice variety, and then analyzed by a geographic information system (GIS). Multiple Linear Regression was applied to factor analysis for the purpose of crop yield estimation based on the factors investigated and obtained by the UAVs. These factors included vegetation indexes (i.e. Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, and Triangular Greenness Index), plant height, and canopy coverage. The prediction of the analysis model was proved to be valid (R2 = 0.99; RMSE = 2.506 g.). Extreme Gradient Boosting (XGBoost) was applied to increase the accuracy of the estimation (RMSE = 0.557 g.; MAE = 0.364). The findings of study showed that the utilization of UAVs could contribute to the estimation of crop yield in the research areas.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44006795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
“Global Positioning System (GPS)” is extensively used for various satellite-based navigation applications. Nowadays, a software-defined GPS receiver has been developed since it is low cost, flexible and able to use in developing more advanced navigation processing algorithms. To implement a GPS software receiver, it is necessary to process three main modules: acquisition, tracking and navigation demodulation for the position, velocity and time (PVT) solution. Signal acquisition estimates the two parameters from the incoming satellite signal, and signal tracking keeps track of them as they change in time. These two parameters are the code phase, the delay time between receiver and satellites, and the Doppler frequency shift instigated by the satellites’ movement relative to the receiver. This study mainly discusses the operation of signal acquisition and tracking. The theoretical study and simulation of the results are described and tested using MATLAB programming language. The input GPS signal is acquired from an antenna system in GPS and an RF front-end portion.
{"title":"Implementation of Signal Acquisition and Tracking for GPS-Based Software Defined Radio Receiver","authors":"","doi":"10.52939/ijg.v19i2.2567","DOIUrl":"https://doi.org/10.52939/ijg.v19i2.2567","url":null,"abstract":"“Global Positioning System (GPS)” is extensively used for various satellite-based navigation applications. Nowadays, a software-defined GPS receiver has been developed since it is low cost, flexible and able to use in developing more advanced navigation processing algorithms. To implement a GPS software receiver, it is necessary to process three main modules: acquisition, tracking and navigation demodulation for the position, velocity and time (PVT) solution. Signal acquisition estimates the two parameters from the incoming satellite signal, and signal tracking keeps track of them as they change in time. These two parameters are the code phase, the delay time between receiver and satellites, and the Doppler frequency shift instigated by the satellites’ movement relative to the receiver. This study mainly discusses the operation of signal acquisition and tracking. The theoretical study and simulation of the results are described and tested using MATLAB programming language. The input GPS signal is acquired from an antenna system in GPS and an RF front-end portion.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47688923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The advent of market liberalization in 1990 brought various forms of transformation, principally land transformation, to Indian cities and their respective peri-urban areas. The Jalandhar city and surrounding rural area from an agrarian state (Punjab) of India provide exemplified setting in this respect. A period of 30 years (1991-2021) has been selected to assess land transformation's magnitude, intensity, and direction. The applicability of different spatial data and methodologies helped analyze the facets of land transformation in quantitative and qualitative terms. The results highlight a massive rural land transformation due to the development of various urban corridors and institutional & commercial set-ups in the city fringe by converting and usurping prime agricultural land and increasing land fragmentation. The built-up class has seen a growth of more than 200 per cent in these decades at the cost of 22 per cent of cropland and other studied classes.
{"title":"Spatially Contextualizing Rural Land Transformation in Peri-Urban Area: A case of Jalandhar City, Punjab (India)","authors":"","doi":"10.52939/ijg.v19i2.2561","DOIUrl":"https://doi.org/10.52939/ijg.v19i2.2561","url":null,"abstract":"The advent of market liberalization in 1990 brought various forms of transformation, principally land transformation, to Indian cities and their respective peri-urban areas. The Jalandhar city and surrounding rural area from an agrarian state (Punjab) of India provide exemplified setting in this respect. A period of 30 years (1991-2021) has been selected to assess land transformation's magnitude, intensity, and direction. The applicability of different spatial data and methodologies helped analyze the facets of land transformation in quantitative and qualitative terms. The results highlight a massive rural land transformation due to the development of various urban corridors and institutional & commercial set-ups in the city fringe by converting and usurping prime agricultural land and increasing land fragmentation. The built-up class has seen a growth of more than 200 per cent in these decades at the cost of 22 per cent of cropland and other studied classes.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42996035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melioidosis is a communicable disease caused by the gram-negative bacterium of Burkholderia pseudomallei. There were founded in soil, water and mammals. In 2017, Thailand was highest a prevalence of Melioidosis at Northeast area. The aimed of this study were to determine the factors affecting the prevalence of Melioidosis. According to sample, there was 265 patients, who has diagnosed by IHA/IFA in 2018-2019. The results showed that the majority of the samples 67.17% were male, 31.32% were agriculture, and 41.89% had underlying disease. The factors associated with the prevalence of disease in the area were: Sex (p-value 0.019, 95%CI = 1.108 - 3.132), household environment (p-value 0.025, 95%CI = 0.163 - 0.885), patient exposure [p-value= 0.001, 95%CI = 0.186 – 0.644), smoking (p-value <0.001, 95%CI = 1.468 – 2.914), underlying disease (p-value<0.001, 95%CI = 1.48 – 4.047), season (p-value 0.016, 95%CI = 1.112 – 2.763), perceived susceptibility (p-value<0.001, 95%CI = 0.207 - 0.726) and perceived severity (p-value 0.005, 95%CI = 0.416 – 0.854). Finally, establishing people aware of the risk of disease combined with these surveillances should be carried out using a geographic map that monitors risk areas, so that can prevent and control Melioidosis appropriately for people at risk area.
{"title":"Geospatial Analysis and Modeling of Melioidosis Prevention and Control in Si Sa Ket Province, Thailand","authors":"","doi":"10.52939/ijg.v19i1.2501","DOIUrl":"https://doi.org/10.52939/ijg.v19i1.2501","url":null,"abstract":"Melioidosis is a communicable disease caused by the gram-negative bacterium of Burkholderia pseudomallei. There were founded in soil, water and mammals. In 2017, Thailand was highest a prevalence of Melioidosis at Northeast area. The aimed of this study were to determine the factors affecting the prevalence of Melioidosis. According to sample, there was 265 patients, who has diagnosed by IHA/IFA in 2018-2019. The results showed that the majority of the samples 67.17% were male, 31.32% were agriculture, and 41.89% had underlying disease. The factors associated with the prevalence of disease in the area were: Sex (p-value 0.019, 95%CI = 1.108 - 3.132), household environment (p-value 0.025, 95%CI = 0.163 - 0.885), patient exposure [p-value= 0.001, 95%CI = 0.186 – 0.644), smoking (p-value <0.001, 95%CI = 1.468 – 2.914), underlying disease (p-value<0.001, 95%CI = 1.48 – 4.047), season (p-value 0.016, 95%CI = 1.112 – 2.763), perceived susceptibility (p-value<0.001, 95%CI = 0.207 - 0.726) and perceived severity (p-value 0.005, 95%CI = 0.416 – 0.854). Finally, establishing people aware of the risk of disease combined with these surveillances should be carried out using a geographic map that monitors risk areas, so that can prevent and control Melioidosis appropriately for people at risk area.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46932648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aims at measuring, analyzing, and evaluating accessibility to health care centers in the city of Al-Mafraq in order to determine their distribution efficiency and their spatial suitability for residential neighborhoods in the city using geographic information systems. It also aims at analyzing the geographical characteristics of the health centers locations in the city and determine their service ranges at the level of the residential neighborhoods in the city (Service Areas, Closest Facility, Location-Allocation, Multiple Ring Buffers). The study found that there is a significant difference and disparity in the ease of access to the health centers in the city of Mafraq. Most of the residential neighborhoods on the outskirts of the city suffer from difficult accessibility to the health centers such as the Industrial City, King Abdullah, Prince Hamzah and Al-Mazzeh neighborhoods in the south; and, Al-Nasr and Al-Jundi neighborhoods in the north. The study also found that the locations of Al-Hussein neighborhood health center and the Southern neighborhood health center were the easiest to reach compared to the other health centers in the city based on the distance and time factors. This research contributes to a better understanding of the geographical accessibility of the population to health care center, helping to identify polarization trends. The results obtained can help decision-makers develop urban planning strategies and optimize investments in health care infrastructure. Future studies will consider the use of other means of transport and other time slots.
{"title":"Measuring Accessibility to Health Care Centers in the City of Al-Mafraq Using Geographic Information Systems","authors":"","doi":"10.52939/ijg.v19i1.2499","DOIUrl":"https://doi.org/10.52939/ijg.v19i1.2499","url":null,"abstract":"This study aims at measuring, analyzing, and evaluating accessibility to health care centers in the city of Al-Mafraq in order to determine their distribution efficiency and their spatial suitability for residential neighborhoods in the city using geographic information systems. It also aims at analyzing the geographical characteristics of the health centers locations in the city and determine their service ranges at the level of the residential neighborhoods in the city (Service Areas, Closest Facility, Location-Allocation, Multiple Ring Buffers). The study found that there is a significant difference and disparity in the ease of access to the health centers in the city of Mafraq. Most of the residential neighborhoods on the outskirts of the city suffer from difficult accessibility to the health centers such as the Industrial City, King Abdullah, Prince Hamzah and Al-Mazzeh neighborhoods in the south; and, Al-Nasr and Al-Jundi neighborhoods in the north. The study also found that the locations of Al-Hussein neighborhood health center and the Southern neighborhood health center were the easiest to reach compared to the other health centers in the city based on the distance and time factors. This research contributes to a better understanding of the geographical accessibility of the population to health care center, helping to identify polarization trends. The results obtained can help decision-makers develop urban planning strategies and optimize investments in health care infrastructure. Future studies will consider the use of other means of transport and other time slots.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47864079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SIFT and SURF image matching were used in many industries such as survey and mapping, geology, medical and automotive. Multispectral sensors offered today become new challenge for researchers to study the performances of SIFT and SURF algorithms on multispectral image. Basically, multispectral image consists of more than three bands. As a result, the differences between those bands leads to nonlinear intensity between images. Both algorithm detectors using ‘blob detector’ that extracting the feature points as a key point for image matching later on. Hence, the less visibility of the feature on the multispectral images was one of the issues need to be solved. Many researchers investigate and propose a new strategy to extract and match the feature point using SIFT and SURF on multispectral image. The image fusions, combinations of different descriptors and revised or alteration of the algorithm themselves were among the approached taken by researchers in order to achieved good results.
{"title":"Multispectral Image Matching Using SIFT and SURF Algorithm: A Review","authors":"","doi":"10.52939/ijg.v19i1.2495","DOIUrl":"https://doi.org/10.52939/ijg.v19i1.2495","url":null,"abstract":"SIFT and SURF image matching were used in many industries such as survey and mapping, geology, medical and automotive. Multispectral sensors offered today become new challenge for researchers to study the performances of SIFT and SURF algorithms on multispectral image. Basically, multispectral image consists of more than three bands. As a result, the differences between those bands leads to nonlinear intensity between images. Both algorithm detectors using ‘blob detector’ that extracting the feature points as a key point for image matching later on. Hence, the less visibility of the feature on the multispectral images was one of the issues need to be solved. Many researchers investigate and propose a new strategy to extract and match the feature point using SIFT and SURF on multispectral image. The image fusions, combinations of different descriptors and revised or alteration of the algorithm themselves were among the approached taken by researchers in order to achieved good results.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43408456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Remote sensing has evolved through the appearance of several approaches. Object-based image analysis is a compelling approach to land use classification, object detection, and change detection in each environment. This paradigm is based on a critical and fundamental segmentation step. However, this step is highly dependent on the determination of the optimal parameters to be achieved. In this sense, methods have been invented to define the optimal segmentation parameters. This article presents an updated review of methods for defining optimal segmentation parameters. For this purpose, pertinent articles published in the main remote sensing journals from the emergence of the concept of object-based image analysis and segmentation to the present were used. The main aim is to provide a precise and detailed review of the different approaches previously presented. The originality of this review resides in the survey of all methods from conventional to the most recent with a discussion of these approaches. The results show that despite the advances in this field of research, most studies use the manual trial-and-error method. Conversely, state-of-the-art methods tend to determine the optimal parameter per type of geographic object and the adaptive calculation of segmentation parameters. Furthermore, the leading methods identified rely on supervised and unsupervised measures similarly, most of which use homogeneity measures. In contrast, a balance between intra- and inter-segment homogeneity and heterogeneity measures are more relevant. A distinction is made between pre-estimation and posterior parameter estimation methods.
{"title":"Determination of Segmentation Parameters for Object-Based Remote Sensing Image Analysis from Conventional to Recent Approaches: A Review","authors":"","doi":"10.52939/ijg.v19i1.2497","DOIUrl":"https://doi.org/10.52939/ijg.v19i1.2497","url":null,"abstract":"Remote sensing has evolved through the appearance of several approaches. Object-based image analysis is a compelling approach to land use classification, object detection, and change detection in each environment. This paradigm is based on a critical and fundamental segmentation step. However, this step is highly dependent on the determination of the optimal parameters to be achieved. In this sense, methods have been invented to define the optimal segmentation parameters. This article presents an updated review of methods for defining optimal segmentation parameters. For this purpose, pertinent articles published in the main remote sensing journals from the emergence of the concept of object-based image analysis and segmentation to the present were used. The main aim is to provide a precise and detailed review of the different approaches previously presented. The originality of this review resides in the survey of all methods from conventional to the most recent with a discussion of these approaches. The results show that despite the advances in this field of research, most studies use the manual trial-and-error method. Conversely, state-of-the-art methods tend to determine the optimal parameter per type of geographic object and the adaptive calculation of segmentation parameters. Furthermore, the leading methods identified rely on supervised and unsupervised measures similarly, most of which use homogeneity measures. In contrast, a balance between intra- and inter-segment homogeneity and heterogeneity measures are more relevant. A distinction is made between pre-estimation and posterior parameter estimation methods.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42685812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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