ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences最新文献_第10页

DECISION-BASED FUSION OF PANSHARPENED VHR SATELLITE IMAGES USING TWO-LEVEL ROLLING SELF-GUIDANCE FILTERING AND EDGE INFORMATION 基于两级滚动自制导滤波和边缘信息的泛锐化VHR卫星图像决策融合

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-691-2023

F. Samadzadegan, A. Toosi, F. Dadrass Javan, A. Stein

引用次数: 0

DEVELOPMENT OF A VOXEL BASED LOCAL PLANE FITTING FOR MULTI-SCALE REGISTRATION OF SEQUENTIAL MLS POINT CLOUDS 基于体素的序列MLS点云多尺度配准局部平面拟合方法研究

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-523-2023

L. Moradi, M. Saadatseresht, P. Shokrzadeh

{"title":"DEVELOPMENT OF A VOXEL BASED LOCAL PLANE FITTING FOR MULTI-SCALE REGISTRATION OF SEQUENTIAL MLS POINT CLOUDS","authors":"L. Moradi, M. Saadatseresht, P. Shokrzadeh","doi":"10.5194/isprs-annals-x-4-w1-2022-523-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-523-2023","url":null,"abstract":"Abstract. The Mobile Laser Scanner (MLS) system is one of the most accurate and fastest data acquisition systems for indoor and outdoor environments mapping. Today, to use this system in an indoor environment where it is impossible to capture GNSS data, Simultaneous Localization and Mapping (SLAM) is used. Most SLAM research has used probabilistic approaches to determine the sensor position and create a map, which leads to drift error in the final result due to their uncertainty. In addition, most SLAM methods give less importance to geometry and mapping concepts. This research aims to solve the SLAM problem by considering the adjustment concepts in mapping and geometrical principles of the environment and proposing an algorithm for reducing drift. For this purpose, a model-based registration is suggested. Correspondence points fall in the same voxel by voxelization, and the registration process is done using a plane model. In this research, two pyramid and simple registration methods are proposed. The results show that the simple registration algorithm is more efficient than the pyramid when the distance between sequential scans is not large otherwise, the pyramid registration is used. In the evaluation, by using simulated data in both pyramid and simple methods, 96.9% and 97.6% accuracy were obtained, respectively. The final test compares the proposed method with a SLAM method and ICP algorithm, which are described further.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83609520","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}

引用次数: 0

THE LARGE-SCALE WILDFIRE SPREAD PREDICTION USING A MULTI-KERNEL CONVOLUTIONAL NEURAL NETWORK 基于多核卷积神经网络的大规模野火蔓延预测

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-483-2023

M. Marjani, M. Mesgari

引用次数: 4

CRUSTAL DEFORMATION IN NW IRAN: INSIGHTS FROM DIFFERENT INVARIANT AND VARIANT COMPONENTS OF GEODETIC STRAIN RATE TENSORS 新伊朗的地壳变形:来自大地应变率张量的不同不变和变分量的启示

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-631-2023

M. Rahmani, V. Nafisi, J. Asgari, A. Nadimi

{"title":"CRUSTAL DEFORMATION IN NW IRAN: INSIGHTS FROM DIFFERENT INVARIANT AND VARIANT COMPONENTS OF GEODETIC STRAIN RATE TENSORS","authors":"M. Rahmani, V. Nafisi, J. Asgari, A. Nadimi","doi":"10.5194/isprs-annals-x-4-w1-2022-631-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-631-2023","url":null,"abstract":"Abstract. Northwest of Iran, as a tectonically active region, has experienced numerous devastating earthquakes. That is why it is so important to study the earth deformation in this area and to provide more precise insights. So far, most researchers have had the preference of using the invariant component of strain rate tensor for investigating the Earth's shape deformation in the region. However, to examine the efficiency of the variant components of the geodesic strain rate tensor in interpreting deformations of north-western Iran, we have in this article maps of variant components of the geodetic strain rate tensor (normal strain rate along north and eastbound). Using the velocity field gathered from a previous article, and also using a simple and straightforward method, the strain rate tensors were calculated. The obtained contraction along the north direction (from the normal strain along this axis) confirms the Eurasia-Arabia collision. Besides, the obtained extension along the east direction and the derived expansion of the dilatation, show the effect of Anatolian motion to the west and eastward movement of the central Iran plateau on the tectonic structure of the studied area. These two results showed that the variant component of strain rate tensor also provides us with useful information about a region shape deformation.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82599609","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}

引用次数: 0

EXPLANATORY ANALYSES OF WORK TRIP GENERATION USING MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) 基于混合地理加权回归(mgwr)的出差生成解释分析

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-707-2023

M. Shahri, M. A. Ghannadi

{"title":"EXPLANATORY ANALYSES OF WORK TRIP GENERATION USING MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR)","authors":"M. Shahri, M. A. Ghannadi","doi":"10.5194/isprs-annals-x-4-w1-2022-707-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-707-2023","url":null,"abstract":"Abstract. In transportation planning, forecasts have commonly followed the sequential four-step model in which, trip generation (production and attraction) plays a critical role. Among the methods applied to model trip generation, regression with Gaussian distribution of errors are recognized as the most prevailing techniques to describe the relationships between production/attraction and explanatory variables by estimating the global, fixed coefficients. Considering that, trip generation is almost impressed by spatial factors which vary over the study area; the main objective of this research is to apply Mixed Geographically Weighted Regression (MGWR) on 253 traffic analysis zones (TAZs) in Mashhad, Iran, by applying travel demand data and relating factors in 2018 to investigate the spatial non-stationarity which are not revealed when global specifications are applied. The influence of certain explanatory variables on response variables may be global, whereas others are local, accordingly, MGWR performs better compared with geographically weighted regression. The results of Moran’s I as spatial autocorrelation index performing on residuals of global, mixed models proved the reliability of the proposed model over the traditional one. The spatial model indicated an improvement in model performance using goodness-of-fit criteria with the coefficient of determination varying from 0.84–0.95 compared with 0.76 and 0.6 in the conventional model. The results of such analysis can provide descriptive and predictive tools at the planning-level for decision-makers.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72497768","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}

引用次数: 0

ENSEMBLING OF DECISION TREES, KNN, AND LOGISTIC REGRESSION WITH SOFT-VOTING METHOD FOR WILDFIRE SUSCEPTIBILITY MAPPING 用软投票方法集成决策树、已知和逻辑回归来绘制野火易感性图

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-647-2023

A. Rezaei Barzani, P. Pahlavani, O. Ghorbanzadeh

{"title":"ENSEMBLING OF DECISION TREES, KNN, AND LOGISTIC REGRESSION WITH SOFT-VOTING METHOD FOR WILDFIRE SUSCEPTIBILITY MAPPING","authors":"A. Rezaei Barzani, P. Pahlavani, O. Ghorbanzadeh","doi":"10.5194/isprs-annals-x-4-w1-2022-647-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-647-2023","url":null,"abstract":"Abstract. As a result of climate change, climatic catastrophes, such as wildfires, are likely to increase. Wildfires continue to occur frequently and spread with greater intensity due to extreme weather conditions. In recent years, explosive fire growths have been reported in the United States, Australia, and other parts of the world. A combination of climate change and human activity has caused the semi-arid forestry areas in Iran's northern provinces to become more desiccated, leading to an increase in wildfires. The accuracy of the resulting fire susceptibility maps (FSMs) will directly be related to the performance of the method classifier. In this study, we use an ensemble classifier to model the FSM for a selected forestry case study area in one of the northern provinces of this country. Therefore, FSM is generated based on established criteria using the ensemble model. With Decision Trees, K nearest neighbor, and Logistic Regression, the ensemble model was created using the soft-voting method. A forest fire inventory data is created based on data collected over five years using GPS and the MODIS thermal anomalies product for training and testing the applied approach. The K-fold method was used for validation, and the resulting FSM was validated using five accuracy assessment metrics. The best result from the area under the curve (AUC) yields 93% for fold 9, and the mean AUC for ten folds yields 88%.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78613080","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}

引用次数: 0

FLOOD SUSCEPTIBILITY MODELLING USING GEOSPATIAL-BASED MULTI-CRITERIA DECISION MAKING IN LARGE SCALE AREAS 基于地理空间多准则决策的大尺度地区洪水易感性模型

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-677-2023

R. Sahraei, Y. Kanani-Sadat, A. Safari, S. Homayouni

{"title":"FLOOD SUSCEPTIBILITY MODELLING USING GEOSPATIAL-BASED MULTI-CRITERIA DECISION MAKING IN LARGE SCALE AREAS","authors":"R. Sahraei, Y. Kanani-Sadat, A. Safari, S. Homayouni","doi":"10.5194/isprs-annals-x-4-w1-2022-677-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-677-2023","url":null,"abstract":"Abstract. Flood is one of the most hazardous natural disasters that cause damages and poses a major threat to human lives and infrastructures worldwide, and its prevention is almost unfeasible. Thus, the detection of flood susceptible areas can be a key to lessen the amount of destruction and mortality. This study aims to implement a framework to identify flood potential zones in an ungauged large-scale area with frequent flood events in recent years. We used two Multi-Criteria Decision Making (MCDM) approaches combined with geospatial analysis, and remote sensing observations for this susceptibility analysis. Nine geomorphological and environmental factors that have an impact on flood behaviour were selected and used for susceptibility modelling. At first, the criteria’s weights were estimated using two MCDM approaches and based on experts’ knowledge. The resultant weights revealed that Flow Accumulation, Topographic wetness index, and Distance to River were the most influential flood susceptibility criteria. After calculating these weights, the criteria’s layers were aggregated through geospatial analysis, which resulted in generating flood susceptibility map. The area under the curve (AUC) and statistical measures such as the Kappa index were used to evaluate the proposed method's efficiency. The validation results illustrate that hybrid FAHP, with AUC= 96.68 and Kappa = 81.36 performed more efficiently than standard AHP, with AUC= 94.53 and Kappa=76.35. Overlaying these maps with the historical flood inventory dataset revealed that 86.43% of flooded areas were categorized as “high” and “very high”. Therefore, the flood susceptibility maps generated through the proposed approach can help the decision-makers and managers allocate the mitigation equipment and facility in data-scarce and ungauged large-scale areas.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86981239","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}

引用次数: 1

SURFACE NORMAL RECONSTRUCTION USING POLARIZATION-UNET 利用偏振网进行表面法向重建

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-537-2023

F. Mortazavi, S. Dajkhosh, M. Saadatseresht

{"title":"SURFACE NORMAL RECONSTRUCTION USING POLARIZATION-UNET","authors":"F. Mortazavi, S. Dajkhosh, M. Saadatseresht","doi":"10.5194/isprs-annals-x-4-w1-2022-537-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-537-2023","url":null,"abstract":"Abstract. Today, three-dimensional reconstruction of objects has many applications in various fields, and therefore, choosing a suitable method for high resolution three-dimensional reconstruction is an important issue and displaying high-level details in three-dimensional models is a serious challenge in this field. Until now, active methods have been used for high-resolution three-dimensional reconstruction. But the problem of active three-dimensional reconstruction methods is that they require a light source close to the object. Shape from polarization (SfP) is one of the best solutions for high-resolution three-dimensional reconstruction of objects, which is a passive method and does not have the drawbacks of active methods. The changes in polarization of the reflected light from an object can be analyzed by using a polarization camera or locating polarizing filter in front of the digital camera and rotating the filter. Using this information, the surface normal can be reconstructed with high accuracy, which will lead to local reconstruction of the surface details. In this paper, an end-to-end deep learning approach has been presented to produce the surface normal of objects. In this method a benchmark dataset has been used to train the neural network and evaluate the results. The results have been evaluated quantitatively and qualitatively by other methods and under different lighting conditions. The MAE value (Mean-Angular-Error) has been used for results evaluation. The evaluations showed that the proposed method could accurately reconstruct the surface normal of objects with the lowest MAE value which is equal to 18.06 degree on the whole dataset, in comparison to previous physics-based methods which are between 41.44 and 49.03 degree.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91174070","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}

引用次数: 0

A SPATIAL PLANNING SUPPORT SYSTEM FOR WIND FARM CONSTRUCTION WITH MACRO AND MICRO PERSPECTIVES 宏观与微观视角的风电场建设空间规划支撑体系

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-13 DOI: 10.5194/isprs-annals-x-4-w1-2022-355-2023

K. Kamkar, H. Motieyan

{"title":"A SPATIAL PLANNING SUPPORT SYSTEM FOR WIND FARM CONSTRUCTION WITH MACRO AND MICRO PERSPECTIVES","authors":"K. Kamkar, H. Motieyan","doi":"10.5194/isprs-annals-x-4-w1-2022-355-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-355-2023","url":null,"abstract":"Abstract. Wind energy is well-known Renewable energy that contributes to countries achieving their sustainable development goals, so governments have planned to increase electricity generation from wind energy in recent years. Selecting suitable places for installing wind farms is one of the most challenging parts of wind energy projects because of the necessity of identifying various parameters, which is considered a site selection problem and needs spatial planning to solve. Furthermore, in constructing a wind farm, finding the optimum placement of wind turbines, known as the wind farm layout optimization (WFLO) problem, is crucial for obtaining maximum total power capacity while minimizing the number of turbines installed. In this research, the analysis is divided into two macro and micro levels to find an efficient and comprehensive approach. In the first level, after removing the restricted area, the MCDM method is used to find the most suitable sites, considering economic, social, and environmental criteria. After selecting one of the most suitable areas located in the middle of Alborz province, the GA algorithm as a meta-heuristic method is employed to solve the WFLO problem at the micro level. The essential criterion in this level is the wake effect among turbines which is simulated according to the Jensen model. As a test case, the selected area was subdivided into 144 cells that, after micro siting, 28 V47-660-45 turbines were placed, which could have a total power of 8732.283 kW capacity.","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74470101","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}

引用次数: 0

FLOOD RISK MAPPING USING RANDOM FOREST AND SUPPORT VECTOR MACHINE 基于随机森林和支持向量机的洪水风险映射

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-13 DOI: 10.5194/isprs-annals-x-4-w1-2022-201-2023

M. Ganjirad, M. R. Delavar

{"title":"FLOOD RISK MAPPING USING RANDOM FOREST AND SUPPORT VECTOR MACHINE","authors":"M. Ganjirad, M. R. Delavar","doi":"10.5194/isprs-annals-x-4-w1-2022-201-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-201-2023","url":null,"abstract":"Abstract. Floods are among the natural disasters that cause financial and human losses all over the world every year. By production of a flood risk map and determination of potential flood risk areas, the possible damages of this phenomenon can be reduced. To map the flood extend in Calcasieu Parish, Louisiana, US, conditioning factors affecting the flood occurrence including elevation, slope, plan curvature, land use, distance from rivers, density of rivers, rainfall, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference built-up index (NDBI) were identified and their information layers produced using the Google Earth Engine (GEE) cloud platform. Then, for flood risk mapping, Random Forest (RF) and support vector machine (SVM) as two machine learning models have been implemented and their results compared. RF and SVM models have been validated based on the maximum absolute error (MAE) index with an accuracy of 0.043 and 0.097, respectively. Visualization of the predicted values in QGIS software confirms that the RF model has provided better outputs than that of the SVM model. By analysing the features importance of the layers in the RF model, it was verified that the elevation, slope, and plan curvature layers have the highest degree of influence on the flood risk with degrees of importance of 0.197, 0.135, and 0.123.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75564176","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}

引用次数: 2