ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences最新文献_第9页

EVALUATION OF STANDARD OCEAN COLOUR CHLOROPHYLL-a ALGORITHMS IN THE NORTHERN PERSIN GULF: COMPARISON OF MAGNITUDE AND SEASONALITY 波斯湾北部标准海洋颜色叶绿素-a算法的评估:幅度和季节性的比较

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-531-2023

M. Moradi, K. Kabiri

{"title":"EVALUATION OF STANDARD OCEAN COLOUR CHLOROPHYLL-a ALGORITHMS IN THE NORTHERN PERSIN GULF: COMPARISON OF MAGNITUDE AND SEASONALITY","authors":"M. Moradi, K. Kabiri","doi":"10.5194/isprs-annals-x-4-w1-2022-531-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-531-2023","url":null,"abstract":"Abstract. The Persian Gulf is a peripheral sea that is quite turbid and visually complicated. Because of the complicated oceanic and atmospheric optical features of this maritime area, satellite remotely sensed chlorophyll-a (Chl-a, mg m−3) outputs have been used extensively. In this study, the seasonality and magnitude of Level-3 Chl-a time-series products, and daily Level-2 satellite-derived Chl-a concentrations were assessed using corresponding/concurrent in-situ observations gathered during 2008 to 2018. The results revealed that the field observations overestimated satellite-derived Chl-a by 115% in the deeper areas and up to 161% along the Iranian coastal waters. Comparison of inter-annual Chl-a time-series datasets with corresponding in-situ measurements showed that temporal patterns of the satellite-derived Chl-a values are not consistent with field observations. The monthly average of the satellite Chl-a series shows a different seasonal pattern in every region of the study area, and their magnitude over-estimated from 21% to 83% relative to in-situ observations. The absorption coefficients at 488–510 nm are primarily influenced by non-living particles instead of phytoplankton pigments, and no significant correlation are found between in-situ and Chl-a values from OC3/OC4 algorithms. Absorption coefficients spectra of water constituent’s shows that the contribution of phytoplankton pigments in particulate absorption coefficients at the blue-green bands are 48%–59%, and the particulate absorptions of water bodies are under the strong influence of non-living particles (40%–52%). Our results suggest that the red-green band-ratios algorithm centred at 675 nm and 555 nm presents more accurate results than OC3/OC4 over the study area.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79308380","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

EFFECT OF KEYFRAMES EXTRACTION FROM THERMAL INFRARED VIDEO STREAM TO GENERATE DENSE POINT CLOUD OF THE BUILDING'S FACADE 从热红外视频流中提取关键帧产生建筑立面密集点云的效果

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-551-2023

S. Motayyeb, F. Samadzadegan, F. Dadrass Javan, H. Hosseinpour

{"title":"EFFECT OF KEYFRAMES EXTRACTION FROM THERMAL INFRARED VIDEO STREAM TO GENERATE DENSE POINT CLOUD OF THE BUILDING'S FACADE","authors":"S. Motayyeb, F. Samadzadegan, F. Dadrass Javan, H. Hosseinpour","doi":"10.5194/isprs-annals-x-4-w1-2022-551-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-551-2023","url":null,"abstract":"Abstract. Keyframes extraction is required and effective for the 3D reconstruction of objects from a thermal video sequence to increase geometric accuracy, reduce the volume of aerial triangulation calculations, and generate the dense point cloud. The primary goal and focus of this paper are to assess the effect of keyframes extraction from the thermal infrared video sequence on the geometric accuracy of the dense point cloud generated. The method of keyframes extraction of thermal infrared video presented in this paper consists of three basic steps. (A) The ability to identify and remove blur frames from non-blur frames in a sequence of recorded frames. (B) The ability to apply the standard baseline condition between sequence frames to establish the overlap condition and prevent the creation of degeneracy conditions. (C) Evaluating degeneracy conditions and keyframes extraction using Geometric Robust Information Criteria (GRIC). The performance evaluation criteria for keyframes extraction in the generation of the thermal infrared dense point cloud in this paper are to assess the increase in density of the generated three-dimensional point cloud and reduce reprojection error. Based on the results and assessments presented in this paper, using keyframes increases the density of the thermal infrared dense point cloud by about 0.03% to 0.10% of points per square meter. It reduces the reprojection error by about 0.005% of pixels (2 times).\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88908735","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

DENSITY-BASED METHOD FOR BUILDING DETECTION FROM LIDAR POINT CLOUD 基于密度的激光雷达点云建筑检测方法

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-423-2023

A. Mahphood, H. Arefi

引用次数: 1

SINGLE IMAGE SUPER RESOLUTION VIA COUPLED SPARSE AND LOW RANK DICTIONARY LEARNING 通过耦合稀疏和低秩字典学习实现单幅图像的超分辨率

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-661-2023

S. Sahebkheir, A. Esmaeily, M. Saba

{"title":"SINGLE IMAGE SUPER RESOLUTION VIA COUPLED SPARSE AND LOW RANK DICTIONARY LEARNING","authors":"S. Sahebkheir, A. Esmaeily, M. Saba","doi":"10.5194/isprs-annals-x-4-w1-2022-661-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-661-2023","url":null,"abstract":"Abstract. Limitations in imaging systems and the effects of changes in sensing have caused limitation in acquiring high resolution images such as satellite images and magnetic resonance imaging (MRI). Sparsity can reduce the noises and improve the resolution. Super resolution in medical and satellite imagery is essential because low resolution image analysis is very difficult. Sparsity techniques have significant influence on computer vision specially when the main objective is extracting the meaningful information. The success of sparsity is related to the nature of signals such as image and sound which are naturally sparse because they were founded based on Wavelet and Fourier equations. In this research, we proposed a method for restoring a clear image from the related low-resolution parts of both MRI and satellite images. First, we proposed a widespread structure for learning the couple low rank and sparse main characteristic representation. Combined optimization of the nuclear and L1 norms extracts the total low rank formation and the local patterns lodged in the image. In that case the reconstructed image will be more informative and matrix decomposition problem can recover a noisy observation matrix into an approximation of low rank matrix and a second matrix which contains some low dimensional structure. We assumed that by removing the blur and noise from these images, they would be reconstructed in the highest quality. The proposed method was compared with a variety dictionary learning approaches which addressed super resolution problem, such as tensor sparsity, Generative Bayesian and TV based methods. We demonstrated the results of applied method on MRI and satellite images, showing both visual and psnr improvements. Dealing with complex data in best manner shows the robustness of the proposed method.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74886074","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

AUTOMATIC EXTRACTION OF CONTROL POINTS FROM 3D LIDAR MOBILE MAPPING AND UAV IMAGERY FOR AERIAL TRIANGULATION 从三维激光雷达移动测绘和无人机图像中自动提取控制点用于空中三角测量

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-581-2023

R. Naimaee, M. Saadatseresht, M. Omidalizarandi

{"title":"AUTOMATIC EXTRACTION OF CONTROL POINTS FROM 3D LIDAR MOBILE MAPPING AND UAV IMAGERY FOR AERIAL TRIANGULATION","authors":"R. Naimaee, M. Saadatseresht, M. Omidalizarandi","doi":"10.5194/isprs-annals-x-4-w1-2022-581-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-581-2023","url":null,"abstract":"Abstract. Installing targets and measuring them as ground control points (GCPs) are time consuming and cost inefficient tasks in a UAV photogrammetry project. This research aims to automatically extract GCPs from 3D LiDAR mobile mapping system (L-MMS) measurements and UAV imagery to perform aerial triangulation in a UAV photogrammetric network. The L-MMS allows to acquire 3D point clouds of an urban environment including floors and facades of buildings with an accuracy of a few centimetres. Integration of UAV imagery, as complementary information enables to reduce the acquisition time of measurement as well as increasing the automation level in a production line. Therefore, a higher quality measurements and more diverse products are obtained. This research hypothesises that the spatial accuracy of the L-MMS is higher than that of the UAV photogrammetric point clouds. The tie points are extracted from the UAV imagery based on the well-known SIFT method, and then matched. The structure from motion (SfM) algorithm is applied to estimate the 3D object coordinates of the matched tie points. Rigid registration is carried out between the point clouds obtained from the L-MMS and the SfM. For each tie point extracted from the SfM point clouds, their corresponding neighbouring points are selected from the L-MMS point clouds, and then a plane is fitted and then a tie point was projected on the plane, and this is how the LiDAR-based control points (LCPs) are calculated. The re-projection error of the analyses carried out on a test data sets of the Glian area in Iran show a half pixel size accuracy standing for a few centimetres range accuracy. Finally, a significant increasing of speed up in survey operations besides improving the spatial accuracy of the extracted LCPs are achieved.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87549783","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 QUICK SEASONAL DETECTION AND ASSESSMENT OF INTERNATIONAL SHADEGAN WETLAND WATER BODY EXTENT USING GOOGLE EARTH ENGINE CLOUD PLATFORM 基于Google earth引擎云平台的国际荫干湿地水体范围季节性快速检测与评价

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-699-2023

S. M. Seyed Mousavi, M. Akhoondzadeh

{"title":"A QUICK SEASONAL DETECTION AND ASSESSMENT OF INTERNATIONAL SHADEGAN WETLAND WATER BODY EXTENT USING GOOGLE EARTH ENGINE CLOUD PLATFORM","authors":"S. M. Seyed Mousavi, M. Akhoondzadeh","doi":"10.5194/isprs-annals-x-4-w1-2022-699-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-699-2023","url":null,"abstract":"Abstract. Understanding the variation of Water Extent (WE) can provide insights into Wetland conservation and management. In this study, and-inter inner-annual variations of WE were analyzed during 2019–2021 to understand the spatiotemporal changes of the International Shadegan Wetland, Iran. We utilized a thresholding process on Modified Normalized Difference Water Index (MNDWI) to extract the WE quickly and accurately using the Google Earth Engine (GEE) platform. The water surface analysis showed that: (1) WE had a downward trend from 2019 to 2021, with the overall average WE being 1405.23 km2; (2) the water area reached its peak due to the water supply to International Shadegan Wetland through the Jarahi River and upstream reservoirs at the end of 2019 and the beginning of 2020, and the largest water body appeared in Winter 2019, reaching 1953.31 km2. In contrast, the smallest water body appeared in Autumn 2021, reaching 563.56 km2; (3) The WE of the wetland showed predictable seasonal characteristics. The water area in Winter was the largest, with an average value of 1829.1 km2, while it was the smallest in Summer, with an average value of 1100.3 km2; (4) The average water area in 2019 was 1490.5 km2 whereas in 2020 and 2021 decreased by 9% and 25%, respectively, and reached 968.6 km2 and 811.9 km2. Finally, to evaluate the proposed model, its results were compared with the Random Forest (RF) classification results. Accordingly, Histogram Analysis (HA) classification achieved 94.6% of the average overall accuracy and the average Kappa coefficient of 0.93, but the RF method obtained 95.38% of the average overall accuracy and an average Kappa coefficient of 0.94.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81465759","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

IMPROVED TORNADO METHOD FOR GROUND POINT FILTERING FROM LIDAR POINT CLOUDS 从激光雷达点云中过滤地点的改进龙卷风方法

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-429-2023

A. Mahphood, H. Arefi

{"title":"IMPROVED TORNADO METHOD FOR GROUND POINT FILTERING FROM LIDAR POINT CLOUDS","authors":"A. Mahphood, H. Arefi","doi":"10.5194/isprs-annals-x-4-w1-2022-429-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-429-2023","url":null,"abstract":"Abstract. In this paper, an improved Tornado method for filtering LiDAR (Light Detection and Ranging) point clouds is presented. The original method uses a vertical cone with a downward vertex and an upward base to remove the points within it as non-ground points. The remaining points are ground points. The cone moves on the ground surface over the entire region of the point cloud. In this work, the regions of the objects are predicted by extracting the vertical features that have points in the vertical plane or vertical column. Therefore, the tornado method is only used in regions that contain objects. In addition, our improved method uses a specific height for a tornado to reduce the Type I error in mountainous areas. Also, a cylinder surrounding the cone is used to reduce the distance calculations between the cone and the point cloud. The results show that this method is very effective and fast compared to the original method. It also has promising results for the Type I error. In addition, this method was tested on the International Society for Photogrammetry and Remote Sensing (ISPRS) datasets and produced outstanding results. The results show that this method achieves high filtering accuracy. Moreover, the proposed method achieves an overall average error of 6.83%, which is lower than most other methods.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85296911","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

FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING 全偏振时间序列图像分析作物类型映射

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-603-2023

H. Noroozi, M. Hasanlou

{"title":"FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING","authors":"H. Noroozi, M. Hasanlou","doi":"10.5194/isprs-annals-x-4-w1-2022-603-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-603-2023","url":null,"abstract":"Abstract. Crop information and quality are not only fundamental for experts using spatial decision support systems but also have many applications in irrigation management, economic analysis for import or export, food safety, and achieving sustainable agriculture. Remote sensing is a cheap and fast way of reaching this goal. Full polarimetric SAR unlike optical sensors is an all-weather system providing geometrical and physical properties of the earth’s surface events. Due to the dynamic changes in crop properties through their phenological stages, crop type mapping has been challenging. As a result, accurate, reliable, and cost-effective crop type mapping using minimum data and processing has been the goal of the remote sensing and precision agriculture community. In this study, a new method based on time series analysis of full polarimetric SAR data combined with radar indices, polarimetric decompositions followed by the three αs extracted from H/A/α decomposition, and unsupervised H/α/Wishart classification bands as features generated from only 5 dates of RADARSAT CONSTELLATION MISSION 2 data were used for classification of crops. Applying random forest and cat boost algorithm as classifiers an accuracy of 87.4% and 75% was respectively achieved. indicating that both algorithms have promising results. Although the random forest algorithm had better results, the cat boost algorithm had less noise in each field and more homogenous farms were detected.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86341597","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

PHOTOGRAMMETRIC RECONSTRUCTION AND MULTI-TEMPORAL COMPARISON OF BRENVA GLACIER (ITALY) FROM ARCHIVE PHOTOS 意大利布伦瓦冰川档案照片的摄影测量重建与多时相比较

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-459-2023

A. Malekian, D. Fugazza, M. Scaioni

{"title":"PHOTOGRAMMETRIC RECONSTRUCTION AND MULTI-TEMPORAL COMPARISON OF BRENVA GLACIER (ITALY) FROM ARCHIVE PHOTOS","authors":"A. Malekian, D. Fugazza, M. Scaioni","doi":"10.5194/isprs-annals-x-4-w1-2022-459-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-459-2023","url":null,"abstract":"Abstract. Since climate change has a significant impact on glaciers, it is essential to track their morphological change by identifying variations in ice mass. In combination with modern photogrammetric approaches, such as Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) dense matching, historical aerial photographs may offer useful information for this objective. Point clouds of the 3D surface of the glaciers may be used to track changes in thickness and height during years. By using appropriate methods for calculating the distances between pairs of point clouds, this operation may be completed. Here, an Alpine glacier massif on Mount Blanc in the Italian Alps was chosen as the case study. National Geographic and the Forestry Institute of France (IGNF) provided seven data sets of digitized analog aerial photos. These were chosen, downloaded, and utilized for photogrammetric analysis. These data sets span almost 40 years, from 1967 to 2006, in total. While the change in ice thickness of these glaciers was relatively small until the mid-1990s, this study revealed an increasing reduction rate at the beginning of 21st century. This paper describes the adopted methodological approach for photogrammetric reconstruction, quality assessment and point cloud comparison. One of the two major glaciers in the considered group (Brenva Glacier) has been focused in this paper as case study.\u0000","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78474207","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

AUTOMATIC ROAD CRACK RECOGNITION BASED ON DEEP LEARNING NETWORKS FROM UAV IMAGERY 基于深度学习网络的无人机图像道路裂缝自动识别

ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences Pub Date : 2023-01-14 DOI: 10.5194/isprs-annals-x-4-w1-2022-685-2023

F. Samadzadegan, F. Dadrass Javan, M. Hasanlou, M. Gholamshahi, F. Ashtari Mahini

{"title":"AUTOMATIC ROAD CRACK RECOGNITION BASED ON DEEP LEARNING NETWORKS FROM UAV IMAGERY","authors":"F. Samadzadegan, F. Dadrass Javan, M. Hasanlou, M. Gholamshahi, F. Ashtari Mahini","doi":"10.5194/isprs-annals-x-4-w1-2022-685-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-4-w1-2022-685-2023","url":null,"abstract":"Abstract. Roads are one of the essential transportation infrastructures that get damaged over time and affect economic development and social activities. Therefore, accurate and rapid recognition of road damage such as cracks is necessary to prevent further damage and repair it in time. The traditional methods for recognizing cracks are using survey vehicles equipped with various sensors, visual inspection of the road surface, and recognition algorithms in image processing. However, performing recognition operations using these methods is associated with high costs and low accuracy and speed. In recent years, the use of deep learning networks in object recognition and visual applications has increased, and these networks have become a suitable alternative to traditional methods. In this paper, the YOLOv4 deep learning network is used to recognize four types of cracks transverse, longitudinal, alligator, and oblique cracks utilizing a set of 2000 RGB visible images. The proposed network with multiple convolutional layers extracts accurate semantic feature maps from input images and classifies road cracks into four classes. This network performs the recognition process with an error of 1% in the training phase and 77% F1-Score, 80% precision, 80% mean average precision (mAP), 77% recall, and 81% intersection over union (IoU) in the testing phase. These results demonstrate the acceptable accuracy and appropriate performance of the model in road crack recognition.","PeriodicalId":30633,"journal":{"name":"ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135833915","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