Pub Date : 2023-11-01DOI: 10.14358/pers.23-00047r2
Ling Tan, Xuelan Ma
Forest fire detection using machine vision has recently emerged as a hot research topic. However, the complexity of background information in smoke images often results in deep learning models losing crucial details while capturing smoke image features. To address this, we present a detection algorithm called Multichannel Smoke YOLOv5s (MCSYOLOv5s). This algorithm comprises a smoke flame detection module, multichannel YOLOv5s (MC‐YOLOv5s), and a smoke cloud classification module, Smoke Classification Network (SCN). MC‐YOLOv5s uses a generative confrontation structure to design a dual‐channel feature extraction network and adopts a new feature cross-fusion mechanism to enhance the smoke feature extraction ability of classic YOLOv5s. The SCN module combines Weather Research and Forecasting numerical forecast results to classify smoke and clouds to reduce false positives caused by clouds. Experimental results demonstrate that our proposed forest fire monitoring method, MCS‐YOLOv5s, achieves higher detection accuracy of 95.17%, surpassing all comparative algorithms. Moreover, it effectively reduces false alarms caused by clouds.
{"title":"An Integrated Approach for Wildfire Photography Telemetry using WRF Numerical Forecast Products","authors":"Ling Tan, Xuelan Ma","doi":"10.14358/pers.23-00047r2","DOIUrl":"https://doi.org/10.14358/pers.23-00047r2","url":null,"abstract":"Forest fire detection using machine vision has recently emerged as a hot research topic. However, the complexity of background information in smoke images often results in deep learning models losing crucial details while capturing smoke image features. To address this, we present a detection algorithm called Multichannel Smoke YOLOv5s (MCSYOLOv5s). This algorithm comprises a smoke flame detection module, multichannel YOLOv5s (MC‐YOLOv5s), and a smoke cloud classification module, Smoke Classification Network (SCN). MC‐YOLOv5s uses a generative confrontation structure to design a dual‐channel feature extraction network and adopts a new feature cross-fusion mechanism to enhance the smoke feature extraction ability of classic YOLOv5s. The SCN module combines Weather Research and Forecasting numerical forecast results to classify smoke and clouds to reduce false positives caused by clouds. Experimental results demonstrate that our proposed forest fire monitoring method, MCS‐YOLOv5s, achieves higher detection accuracy of 95.17%, surpassing all comparative algorithms. Moreover, it effectively reduces false alarms caused by clouds.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"49 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.14358/pers.23-00024r2
Xiong Xu, Tao Cheng, Beibei Zhao, Chao Wang, Xiaohua Tong, Yongjiu Feng, Huan Xie, Yanmin Jin
Rapid detection of solid waste with remote sensing images is of great significance for environmental protection. In recent years, deep learning-based object detection methods have been widely studied. In contrast to regular objects such as airplanes or buildings, solid wastes commonly h ave arbitrary shapes with difficult‐to‐distinguish boundaries. In this study, a solid waste detection network with a weighted deformable convolution and a global context block based on Feature Pyramid Network (FPN) model was proposed. The designed feature extraction structure can help to enhance the boundary and shape features of solid waste. The effectiveness of the proposed method was verified on the well-known DetectIon in Optical Remote sensing images data set and further on a solid waste data set, which was collected by the authors manually. The experimental results show that the proposed method outperforms other traditional object detection methods and a maximum improvement of 5.27% was obtained compared to the FPN method.
{"title":"A Novel Object Detection Method for Solid Waste Incorporating a Weighted Deformable Convolution","authors":"Xiong Xu, Tao Cheng, Beibei Zhao, Chao Wang, Xiaohua Tong, Yongjiu Feng, Huan Xie, Yanmin Jin","doi":"10.14358/pers.23-00024r2","DOIUrl":"https://doi.org/10.14358/pers.23-00024r2","url":null,"abstract":"Rapid detection of solid waste with remote sensing images is of great significance for environmental protection. In recent years, deep learning-based object detection methods have been widely studied. In contrast to regular objects such as airplanes or buildings, solid wastes commonly h ave arbitrary shapes with difficult‐to‐distinguish boundaries. In this study, a solid waste detection network with a weighted deformable convolution and a global context block based on Feature Pyramid Network (FPN) model was proposed. The designed feature extraction structure can help to enhance the boundary and shape features of solid waste. The effectiveness of the proposed method was verified on the well-known DetectIon in Optical Remote sensing images data set and further on a solid waste data set, which was collected by the authors manually. The experimental results show that the proposed method outperforms other traditional object detection methods and a maximum improvement of 5.27% was obtained compared to the FPN method.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"49 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.14358/pers.23-00009r2
Kanaya Dutta, Debolina Basu, Sonam Agrawal
The incessant rise of artificial surfaces has increased the temperatures of cities, distressing urban health and sustainability. Fast-growing tropical cities particularly call for an understanding of this phenomenon, known as the urban heat island (UHI). The present study was conducted to detect UHI dynamics over the National Capital Region of India. Stretching over more than 32 000 km 2 , this region consists of urban centers of varying sizes. Landsat thermal bands were processed to extract temperature patterns between 1999 and 2019. Urban climate change was prominent, as a 2349-km 2 expansion in UHI area was spotted. Urban clusters of different sizes were demarcated by applying the k-nearest neighbor algorithm on the normalized difference building index maps. This empirical analysis helped to form a logarithmic relation between city size and UHI intensity. Observed results set a framework to assess the thermal environment of numerous urban centers from any tropical country. UHI intensity values for various city sizes were computed, as they were crucial to decide the outdoor comfort zones based on the base temperature conditions of other cities. Further, the critical zones in each urban cluster were identified using the vegetation index, and scopes of landscaping were suggested based on the observed building morphologies of different local climate zones.
{"title":"Identification of Critical Urban Clusters for Placating Urban Heat Island Effects over Fast-Growing Tropical City Regions: Estimating the Contribution of Different City Sizes in Escalating UHI Intensity","authors":"Kanaya Dutta, Debolina Basu, Sonam Agrawal","doi":"10.14358/pers.23-00009r2","DOIUrl":"https://doi.org/10.14358/pers.23-00009r2","url":null,"abstract":"The incessant rise of artificial surfaces has increased the temperatures of cities, distressing urban health and sustainability. Fast-growing tropical cities particularly call for an understanding of this phenomenon, known as the urban heat island (UHI). The present study was conducted to detect UHI dynamics over the National Capital Region of India. Stretching over more than 32 000 km 2 , this region consists of urban centers of varying sizes. Landsat thermal bands were processed to extract temperature patterns between 1999 and 2019. Urban climate change was prominent, as a 2349-km 2 expansion in UHI area was spotted. Urban clusters of different sizes were demarcated by applying the k-nearest neighbor algorithm on the normalized difference building index maps. This empirical analysis helped to form a logarithmic relation between city size and UHI intensity. Observed results set a framework to assess the thermal environment of numerous urban centers from any tropical country. UHI intensity values for various city sizes were computed, as they were crucial to decide the outdoor comfort zones based on the base temperature conditions of other cities. Further, the critical zones in each urban cluster were identified using the vegetation index, and scopes of landscaping were suggested based on the observed building morphologies of different local climate zones.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"47 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.14358/pers.23-00046r2
Wuyong Tao, Dong Xu, Xijiang Chen, Ge Tan
Correspondence selection is an indispensable process in point cloud registration. The success of point cloud registration largely depends on a good correspondence selection method. For this purpose, a novel correspondence selection method is proposed in this paper. First, two geometric constraints, one of which is proposed in this paper, are used to compute the compatibility score between two correspondences. Then, the feature vectors of the correspondences are constructed according to the compatibility scores between the correspondence and others. A support vector machine classifier is trained to classify the correct and incorrect correspondences by using the feature vectors. The experimental results demonstrate that our method can choose the right correspondences well and get high precision and F-score performance. Also, our method has the best robustness to noise, pointdensity variation, and partial overlap compared to the other methods.
{"title":"A Powerful Correspondence Selection Method for Point Cloud Registration Based on Machine Learning","authors":"Wuyong Tao, Dong Xu, Xijiang Chen, Ge Tan","doi":"10.14358/pers.23-00046r2","DOIUrl":"https://doi.org/10.14358/pers.23-00046r2","url":null,"abstract":"Correspondence selection is an indispensable process in point cloud registration. The success of point cloud registration largely depends on a good correspondence selection method. For this purpose, a novel correspondence selection method is proposed in this paper. First, two geometric constraints, one of which is proposed in this paper, are used to compute the compatibility score between two correspondences. Then, the feature vectors of the correspondences are constructed according to the compatibility scores between the correspondence and others. A support vector machine classifier is trained to classify the correct and incorrect correspondences by using the feature vectors. The experimental results demonstrate that our method can choose the right correspondences well and get high precision and F-score performance. Also, our method has the best robustness to noise, pointdensity variation, and partial overlap compared to the other methods.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"47 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ASPRS Positional Accuracy Standards for Digital Geospatial Data","authors":"","doi":"10.14358/pers.89.10.589","DOIUrl":"https://doi.org/10.14358/pers.89.10.589","url":null,"abstract":"","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A cartographer acquaintance of mine once told me that when a map is on a coffee table and no one picks it up to examine, it is just a piece of paper. So, in an effort to help others learn tricks of the trade which draw attention to your map, to follow up on the past two columns on customizing text and colors on your maps, and to continue the theme of “never accepting the defaults”, I asked two experienced map makers/cartographers to share some of the things they use to make their maps more “mappy”. When pushing the art-envelope in cartography, attention to detail can be the difference between a map that sits on the coffee table, a good map, and great one.
{"title":"GIS&Tips ‐ Tricks Making Your Maps more “Mappy Maps”","authors":"Shira A. Ellenson, Yolani Martin, Al Karlin","doi":"10.14358/pers.89.10.595","DOIUrl":"https://doi.org/10.14358/pers.89.10.595","url":null,"abstract":"A cartographer acquaintance of mine once told me that when a map is on a coffee table and no one picks it up to examine, it is just a piece of paper. So, in an effort to help others learn tricks of the trade which draw attention to your map, to follow up on the past two columns on customizing text and colors on your maps, and to continue the theme of “never accepting the defaults”, I asked two experienced map makers/cartographers to share some of the things they use to make their maps more “mappy”. When pushing the art-envelope in cartography, attention to detail can be the difference between a map that sits on the coffee table, a good map, and great one.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The ASPRS Positional Accuracy Standards, Edition 2: The Geospatial Mapping Industry Guide to Best Practices","authors":"Qassim Abdullah","doi":"10.14358/pers.89.10.581","DOIUrl":"https://doi.org/10.14358/pers.89.10.581","url":null,"abstract":"","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban vegetation phenology is important for understanding the relationship between human activities on urban ecosystems and carbon cycle. The relationship between urban and rural vegetation phenology and environmental and meteorological factors were studied across urban-rural gradients. However, the relationship of intra-urban urbanization intensity (UI) gradients on vegetation at the start of season (SOS) is unclear. Here, we used remote sensing data to quantitatively assess the relationship of vegetation SOS to UI gradients at mid-high latitudes in the northern hemisphere. The results showed that urban area vegetation SOS widely presented earlier than for rural area vegetation. Across the cities we investigated the extent UI gradient was prevalent as a threshold (33.2% ± 2.3%) of surface temperature to SOS advance enhancement and offset. At low urbanization enhanced surface temperature on sos advances, while at high urbanization offset surface temperature on SOS advances. Overall, UI demonstrated a nonlinear relationship with sos. The results of this study suggest that there may be thresholds of impact on vegetation SOS in future global climate and environment change processes, where opposite effects can occur below and above thresholds.
{"title":"Different Urbanization Levels Lead to Divergent Responses of Spring Phenology","authors":"Chaoya Dang, Zhenfeng Shao, Xiao Huang, Gui Cheng, Jiaxin Qian","doi":"10.14358/pers.23-00008r2","DOIUrl":"https://doi.org/10.14358/pers.23-00008r2","url":null,"abstract":"Urban vegetation phenology is important for understanding the relationship between human activities on urban ecosystems and carbon cycle. The relationship between urban and rural vegetation phenology and environmental and meteorological factors were studied across urban-rural gradients. However, the relationship of intra-urban urbanization intensity (UI) gradients on vegetation at the start of season (SOS) is unclear. Here, we used remote sensing data to quantitatively assess the relationship of vegetation SOS to UI gradients at mid-high latitudes in the northern hemisphere. The results showed that urban area vegetation SOS widely presented earlier than for rural area vegetation. Across the cities we investigated the extent UI gradient was prevalent as a threshold (33.2% ± 2.3%) of surface temperature to SOS advance enhancement and offset. At low urbanization enhanced surface temperature on sos advances, while at high urbanization offset surface temperature on SOS advances. Overall, UI demonstrated a nonlinear relationship with sos. The results of this study suggest that there may be thresholds of impact on vegetation SOS in future global climate and environment change processes, where opposite effects can occur below and above thresholds.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.14358/pers.23-00012r2
Sheng Wang, Taixia Wu, Qiang Shen
Lotus wetland is a type of wetland that can efficiently purify water. Therefore, rapid and accurate remote sensing monitoring of the distribution of lotus wetland has great significance to their conservation and the promotion of a sustainable and healthy development of ecosystems. The phenology-based method has proven effective in mapping some different types of wetlands. However, because of the serious absence of remote sensing data caused by cloud coverage and the differences in the phenological rhythms of lotus wetlands in different areas, achieving high-precision mapping of different regions using a unified approach is a challenge. To address the issue, this article proposes a Phenology Normalized Lotus Index (PNLI) model that combines SAR time-series imagery and the phenology-based method. The results of this study demonstrate that the PNLI model shows good applicability in different areas and has high mapping accuracy. The model can map the lotus wetland distribu tion in large areas quickly and simultaneously with high precision.
{"title":"Mapping Lotus Wetland Distribution with the Phenology Normalized Lotus Index Using SAR Time-Series Imagery and the Phenology-Based Method","authors":"Sheng Wang, Taixia Wu, Qiang Shen","doi":"10.14358/pers.23-00012r2","DOIUrl":"https://doi.org/10.14358/pers.23-00012r2","url":null,"abstract":"Lotus wetland is a type of wetland that can efficiently purify water. Therefore, rapid and accurate remote sensing monitoring of the distribution of lotus wetland has great significance to their conservation and the promotion of a sustainable and healthy development of ecosystems. The phenology-based method has proven effective in mapping some different types of wetlands. However, because of the serious absence of remote sensing data caused by cloud coverage and the differences in the phenological rhythms of lotus wetlands in different areas, achieving high-precision mapping of different regions using a unified approach is a challenge. To address the issue, this article proposes a Phenology Normalized Lotus Index (PNLI) model that combines SAR time-series imagery and the phenology-based method. The results of this study demonstrate that the PNLI model shows good applicability in different areas and has high mapping accuracy. The model can map the lotus wetland distribu tion in large areas quickly and simultaneously with high precision.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135367681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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