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Large-Scale LoD2 Building Modeling using Deep Multimodal Feature Fusion 基于深度多模态特征融合的大规模LoD2建筑建模
IF 2.6 4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-07-12 DOI: 10.1080/07038992.2023.2236243
Faezeh Soleimani Vostikolaei, S. Jabari
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引用次数: 0
Attributing a Causal Agent and Assessing the Severity of Non-Stand Replacing Disturbances in a Northern Hardwood Forest using Landsat-Derived Vegetation Indices 利用陆地卫星衍生植被指数确定原因并评估北方阔叶林非林分替代干扰的严重程度
IF 2.6 4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-03-28 DOI: 10.1080/07038992.2023.2196356
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引用次数: 0
Water Bottom and Surface Classification Algorithm for Bathymetric LiDAR Point Clouds of Very Shallow Waters 极浅水水深激光雷达点云的水面和底面分类算法
IF 2.6 4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-02-15 DOI: 10.1080/07038992.2023.2172957
Hyejin Kim, Jaehoon Jung, Jaebin Lee, Gwangjae Wie
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引用次数: 0
Sensitivity Analysis of Parameters of U-Net Model for Semantic Segmentation of Silt Storage Dams from Remote Sensing Images 基于遥感图像的淤地坝语义分割U-Net模型参数敏感性分析
IF 2.6 4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-02-09 DOI: 10.1080/07038992.2023.2178834
J. Hou, B. Hou, Moyan Zhu, Ji Zhou, Qiong Tian
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引用次数: 0
Radarsat Constellation Mission Derived Winter Glacier Velocities for the St. Elias Icefield, Yukon/Alaska: 2022 and 2023 Radarsat星座任务推导出育空/阿拉斯加圣埃利亚斯冰原冬季冰川速度:2022年和2023年
4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-01-02 DOI: 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.
在这里,我们使用2022年和2023年冬季获得的高分辨率(5米)雷达卫星星座任务(RCM)图像来确定育空/阿拉斯加圣埃利亚斯冰原冰川的运动。我们的区域速度图在很大程度上与之前的研究一致,育空地区的冰川向南和向西流向阿拉斯加海岸的冰川运动更快(约600米/年),而在育空地区东部和东北部流向陆地的冰川运动相对较慢(100-400米/年)。我们还在冰原内发现了两个新的冰川涌流:2022年冬季Nàłùdäy (Lowell)冰川的涌流和2023年冬季Chitina冰川的涌流,并利用~ 4天重复RCM图像跟踪了每次涌流从1月到3月的进展。为了评估rcm得出的速度的质量,我们将结果与位于育空地区两个冰川上的三个冰上dGPS站的50个同时测量结果进行了比较,发现测量结果之间的平均绝对差为6.6米/年。我们的研究结果证明了RCM数据在确定具有复杂地形的大区域冰川运动方面的实用性,支持基于过程的快速流动和涌浪型冰川研究,并继续Radarsat-2任务获得的速度产品的遗产。
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引用次数: 0
A Novel Classification Method for PolSAR Image Combining the Deep Learning Model and Adaptive Boosting of Shallow Classifiers 一种结合深度学习模型和浅分类器自适应增强的PolSAR图像分类新方法
4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-01-02 DOI: 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.
偏振合成孔径雷达(PolSAR)图像的分类主要是根据地物的后向散射信息。对于后向散射信息复杂的区域,容易出现误分类,这给提高PolSAR图像的分类精度带来了挑战。针对这种情况,本文将深度学习模型与传统分类器相结合,对PolSAR图像进行分类。首先,利用卷积神经网络(CNN)对PolSAR图像进行分类,根据像素的类别预测概率定位容易被误分类的关键像素;然后,自适应增强(AdaBoost)算法将三个浅分类器(支持向量机(SVM)、Wishart和决策树分类器)组合成强分类器,对关键像素进行重分类。最后输出关键像素和其他像素的标签,作为最终的分类结果。在两幅PolSAR图像上的实验表明,该方法可以提高分类性能,获得较好的分类效果。
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引用次数: 0
UAV-SfM and Geographic Object-Based Image Analysis for Measuring Multi-Temporal Planimetric and Volumetric Erosion of Arctic Coasts 无人机- sfm和基于地理目标的图像分析用于测量北极海岸的多时相平面和体积侵蚀
IF 2.6 4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-01-02 DOI: 10.1080/07038992.2023.2211679
A. Clark, B. Moorman, D. Whalen
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引用次数: 0
Passive Microwave Sea Ice Edge Displacement Error over the Eastern Canadian Arctic for the period 2013-2021 2013-2021年加拿大东部北极被动微波海冰边缘位移误差
IF 2.6 4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-01-02 DOI: 10.1080/07038992.2023.2205531
A. Soleymani, N. Saberi, K. A. Scott
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引用次数: 1
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 基于卷积神经网络的RADARSAT-2双极化ScanSAR场景参数化海冰分类精度评估:以加拿大Coronation Gulf为例
4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-01-02 DOI: 10.1080/07038992.2023.2247091
Benoit Montpetit, Benjamin Deschamps, Joshua King, Jason Duffe
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引用次数: 0
Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities 基于多源遥感的北方地区植被生产力模型研究机会
4区 地球科学 Q3 REMOTE SENSING Pub Date : 2023-01-02 DOI: 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.
了解在北方生态系统中驱动陆地植被生产力动态的过程对于准确评估碳动态至关重要。监测这些动态通常需要融合大尺度遥感观测、气候信息和其他地理空间数据输入,这些数据往往存在未知误差,难以获得,或限制生产力估算的时空分辨率。在过去的十年里,遥感技术和观测波长的多样性取得了显著的进步,为植被碳动态提供了新的见解。在本文中,我们回顾了目前陆地植被生产力建模的主要方法,然后讨论了新的遥感仪器和衍生产品,包括Sentinel-3陆地表面温度,土壤水分和海洋盐度(SMOS)任务的冻结和解冻状态,以及土壤水分主动式被动(SMAP)任务的土壤水分。我们概述了这些产品如何改善完全由遥感观测融合驱动的北方生产力估算的空间细节和时间表征。最后,我们展示了如何将这些不同的元素整合到哈德逊平原的主要土地覆盖类型中,哈德逊平原是加拿大北方地区广泛的湿地主导地区,并为未来的模型开发提供了建议。
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引用次数: 0
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Canadian Journal of Remote Sensing
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