Pub Date : 2023-07-12DOI: 10.1080/07038992.2023.2236243
Faezeh Soleimani Vostikolaei, S. Jabari
{"title":"Large-Scale LoD2 Building Modeling using Deep Multimodal Feature Fusion","authors":"Faezeh Soleimani Vostikolaei, S. Jabari","doi":"10.1080/07038992.2023.2236243","DOIUrl":"https://doi.org/10.1080/07038992.2023.2236243","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49389514","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 : 2023-03-28DOI: 10.1080/07038992.2023.2196356
{"title":"Attributing a Causal Agent and Assessing the Severity of Non-Stand Replacing Disturbances in a Northern Hardwood Forest using Landsat-Derived Vegetation Indices","authors":"","doi":"10.1080/07038992.2023.2196356","DOIUrl":"https://doi.org/10.1080/07038992.2023.2196356","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44959193","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 : 2023-02-15DOI: 10.1080/07038992.2023.2172957
Hyejin Kim, Jaehoon Jung, Jaebin Lee, Gwangjae Wie
{"title":"Water Bottom and Surface Classification Algorithm for Bathymetric LiDAR Point Clouds of Very Shallow Waters","authors":"Hyejin Kim, Jaehoon Jung, Jaebin Lee, Gwangjae Wie","doi":"10.1080/07038992.2023.2172957","DOIUrl":"https://doi.org/10.1080/07038992.2023.2172957","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42237399","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 : 2023-02-09DOI: 10.1080/07038992.2023.2178834
J. Hou, B. Hou, Moyan Zhu, Ji Zhou, Qiong Tian
{"title":"Sensitivity Analysis of Parameters of U-Net Model for Semantic Segmentation of Silt Storage Dams from Remote Sensing Images","authors":"J. Hou, B. Hou, Moyan Zhu, Ji Zhou, Qiong Tian","doi":"10.1080/07038992.2023.2178834","DOIUrl":"https://doi.org/10.1080/07038992.2023.2178834","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49526033","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 : 2023-01-02DOI: 10.1080/07038992.2023.2264395
Wesley Van Wychen, Courtney Bayer, Luke Copland, Erika Brummell, Christine Dow
Here we use high resolution (5 m) Radarsat Constellation Mission (RCM) imagery acquired in winters 2022 and 2023 to determine motion across glaciers of the St. Elias Icefield in Yukon/Alaska. Our regional velocity mapping largely conforms with previous studies, with faster motion (>600 m/yr) for the glaciers originating in the Yukon that drain southward and westward to the coast of Alaska and relatively slower motion (100–400 m/yr) for the land terminating glaciers that drain eastward and northeastward and stay within the Yukon. We also identify two new glacier surges within the icefields: the surge of Nàłùdäy (Lowell) Glacier in Winter 2022, and Chitina Glacier in Winter 2023, and track the progression of each surge from January to March utilizing ∼4-day repeat RCM imagery. To evaluate the quality of RCM-derived velocities, we compare our results with 50 simultaneous measurements at three on-ice dGPS stations located on two Yukon glaciers and find the average absolute difference between measurements to be 6.6 m/yr. Our results demonstrate the utility of RCM data to determine glacier motion across large regions with complex topography, to support process-based studies of fast flowing and surge-type glaciers and continue the legacy of velocity products derived from the Radarsat-2 mission.
{"title":"Radarsat Constellation Mission Derived Winter Glacier Velocities for the St. Elias Icefield, Yukon/Alaska: 2022 and 2023","authors":"Wesley Van Wychen, Courtney Bayer, Luke Copland, Erika Brummell, Christine Dow","doi":"10.1080/07038992.2023.2264395","DOIUrl":"https://doi.org/10.1080/07038992.2023.2264395","url":null,"abstract":"Here we use high resolution (5 m) Radarsat Constellation Mission (RCM) imagery acquired in winters 2022 and 2023 to determine motion across glaciers of the St. Elias Icefield in Yukon/Alaska. Our regional velocity mapping largely conforms with previous studies, with faster motion (>600 m/yr) for the glaciers originating in the Yukon that drain southward and westward to the coast of Alaska and relatively slower motion (100–400 m/yr) for the land terminating glaciers that drain eastward and northeastward and stay within the Yukon. We also identify two new glacier surges within the icefields: the surge of Nàłùdäy (Lowell) Glacier in Winter 2022, and Chitina Glacier in Winter 2023, and track the progression of each surge from January to March utilizing ∼4-day repeat RCM imagery. To evaluate the quality of RCM-derived velocities, we compare our results with 50 simultaneous measurements at three on-ice dGPS stations located on two Yukon glaciers and find the average absolute difference between measurements to be 6.6 m/yr. Our results demonstrate the utility of RCM data to determine glacier motion across large regions with complex topography, to support process-based studies of fast flowing and surge-type glaciers and continue the legacy of velocity products derived from the Radarsat-2 mission.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135798449","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 : 2023-01-02DOI: 10.1080/07038992.2023.2257331
Yan Duan, Shaojie Bai, Limin Liu, Guangwei Wang
Polarimetric synthetic aperture radar (PolSAR) images are classified mainly according to the backscattering information of ground objects. For regions with complex backscattering information, misclassification is easy to occur, which leads to challenges in improving the classification accuracy of the PolSAR image. Given this situation, this paper combines the Deep Learning Model and traditional classifiers to classify PolSAR image. First, the Convolution Neural Network (CNN) was used to classify the PolSAR image and according to the category prediction probability of pixels, the key pixels easily misclassified are located. Then, the adaptive boosting (AdaBoost) algorithm combined the three shallow classifiers (the Support Vector Machine (SVM), the Wishart and the Decision Tree classifier) into strong classifiers to reclassify the key pixels. Finally, the labels of key pixels and other pixels are output as the final classification result. Experiments on two PolSAR images show that the proposed method can improve classification performance and obtain better classification results.
{"title":"A Novel Classification Method for PolSAR Image Combining the Deep Learning Model and Adaptive Boosting of Shallow Classifiers","authors":"Yan Duan, Shaojie Bai, Limin Liu, Guangwei Wang","doi":"10.1080/07038992.2023.2257331","DOIUrl":"https://doi.org/10.1080/07038992.2023.2257331","url":null,"abstract":"Polarimetric synthetic aperture radar (PolSAR) images are classified mainly according to the backscattering information of ground objects. For regions with complex backscattering information, misclassification is easy to occur, which leads to challenges in improving the classification accuracy of the PolSAR image. Given this situation, this paper combines the Deep Learning Model and traditional classifiers to classify PolSAR image. First, the Convolution Neural Network (CNN) was used to classify the PolSAR image and according to the category prediction probability of pixels, the key pixels easily misclassified are located. Then, the adaptive boosting (AdaBoost) algorithm combined the three shallow classifiers (the Support Vector Machine (SVM), the Wishart and the Decision Tree classifier) into strong classifiers to reclassify the key pixels. Finally, the labels of key pixels and other pixels are output as the final classification result. Experiments on two PolSAR images show that the proposed method can improve classification performance and obtain better classification results.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135799414","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 : 2023-01-02DOI: 10.1080/07038992.2023.2211679
A. Clark, B. Moorman, D. Whalen
{"title":"UAV-SfM and Geographic Object-Based Image Analysis for Measuring Multi-Temporal Planimetric and Volumetric Erosion of Arctic Coasts","authors":"A. Clark, B. Moorman, D. Whalen","doi":"10.1080/07038992.2023.2211679","DOIUrl":"https://doi.org/10.1080/07038992.2023.2211679","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45050553","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 : 2023-01-02DOI: 10.1080/07038992.2023.2205531
A. Soleymani, N. Saberi, K. A. Scott
{"title":"Passive Microwave Sea Ice Edge Displacement Error over the Eastern Canadian Arctic for the period 2013-2021","authors":"A. Soleymani, N. Saberi, K. A. Scott","doi":"10.1080/07038992.2023.2205531","DOIUrl":"https://doi.org/10.1080/07038992.2023.2205531","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47865506","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 : 2023-01-02DOI: 10.1080/07038992.2023.2247091
Benoit Montpetit, Benjamin Deschamps, Joshua King, Jason Duffe
{"title":"Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada","authors":"Benoit Montpetit, Benjamin Deschamps, Joshua King, Jason Duffe","doi":"10.1080/07038992.2023.2247091","DOIUrl":"https://doi.org/10.1080/07038992.2023.2247091","url":null,"abstract":"","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135799634","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 : 2023-01-02DOI: 10.1080/07038992.2023.2256895
Ramon Melser, Nicholas C. Coops, Michael A. Wulder, Chris Derksen
Understanding the processes driving terrestrial vegetation productivity dynamics in boreal ecosystems is critical for accurate assessments of carbon dynamics. Monitoring these dynamics typically requires a fusion of broad-scale remote sensing observations, climate information and other geospatial data inputs, which often have unknown errors, are difficult to obtain, or limit spatial and temporal resolutions of productivity estimates. The past decade has seen notable advances in technologies and the diversity of observed wavelengths from remote sensing instruments, offering new insights on vegetation carbon dynamics. In this communication, we review key current approaches for modeling terrestrial vegetation productivity, followed by a discussion on new remote sensing instruments and derived products including Sentinel-3 Land Surface Temperature, freeze & thaw state from the Soil Moisture & Ocean Salinity (SMOS) mission, and soil moisture from the Soil Moisture Active Passive (SMAP) mission. We outline how these products can improve the spatial detail and temporal representation of boreal productivity estimates driven entirely by a fusion of remote sensing observations. We conclude with a demonstration of how these different elements can be integrated across key land cover types in the Hudson plains, an extensive wetland-dominated region of the Canadian boreal, and provide recommendations for future model development.
{"title":"Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities","authors":"Ramon Melser, Nicholas C. Coops, Michael A. Wulder, Chris Derksen","doi":"10.1080/07038992.2023.2256895","DOIUrl":"https://doi.org/10.1080/07038992.2023.2256895","url":null,"abstract":"Understanding the processes driving terrestrial vegetation productivity dynamics in boreal ecosystems is critical for accurate assessments of carbon dynamics. Monitoring these dynamics typically requires a fusion of broad-scale remote sensing observations, climate information and other geospatial data inputs, which often have unknown errors, are difficult to obtain, or limit spatial and temporal resolutions of productivity estimates. The past decade has seen notable advances in technologies and the diversity of observed wavelengths from remote sensing instruments, offering new insights on vegetation carbon dynamics. In this communication, we review key current approaches for modeling terrestrial vegetation productivity, followed by a discussion on new remote sensing instruments and derived products including Sentinel-3 Land Surface Temperature, freeze & thaw state from the Soil Moisture & Ocean Salinity (SMOS) mission, and soil moisture from the Soil Moisture Active Passive (SMAP) mission. We outline how these products can improve the spatial detail and temporal representation of boreal productivity estimates driven entirely by a fusion of remote sensing observations. We conclude with a demonstration of how these different elements can be integrated across key land cover types in the Hudson plains, an extensive wetland-dominated region of the Canadian boreal, and provide recommendations for future model development.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135799420","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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