Building segmentation through a gated graph convolutional neural network with deep structured feature embedding [0.03%]
基于门控图卷积神经网络的深度结构特征嵌入建筑分割方法
Yilei Shi,Qingyu Li,Xiao Xiang Zhu
Yilei Shi
Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural...
Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction [0.03%]
基于图的标签传播学习：半监督高光谱降维的多任务迭代回归框架
Danfeng Hong,Naoto Yokoya,Jocelyn Chanussot et al.
Danfeng Hong et al.
Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-level data analysis, has been garnering growing attention in the remote sensing community. Although a variety of methods, both unsupervised and supe...
Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network [0.03%]
基于局部气候区的多季节Sentinel-2城市地物分类研究（递归残差网络）
Chunping Qiu,Lichao Mou,Michael Schmitt et al.
Chunping Qiu et al.
The local climate zone (LCZ) scheme was originally proposed to provide an interdisciplinary taxonomy for urban heat island (UHI) studies. In recent years, the scheme has also become a starting point for the development of higher-level produ...
Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests [0.03%]
热带雨林地面激光雷达点云中的藤本植物茎干半自动提取算法
Sruthi M Krishna Moorthy,Yunfei Bao,Kim Calders et al.
Sruthi M Krishna Moorthy et al.
Lianas are key structural elements of tropical forests having a large impact on the global carbon cycle by reducing tree growth and increasing tree mortality. Despite the reported increasing abundance of lianas across neotropics, very few s...
Satellite-based view of the aerosol spatial and temporal variability in the Córdoba region (Argentina) using over ten years of high-resolution data [0.03%]
基于十年以上高分辨率数据的卫星观测在阿根廷科尔多瓦地区气溶胶时空变化研究
Lara Sofía Della Ceca,María Fernanda García Ferreyra,Alexei Lyapustin et al.
Lara Sofía Della Ceca et al.
Space-based observations offer a unique opportunity to investigate the atmosphere and its changes over decadal time scales, particularly in regions lacking in situ and/or ground based observations. In this study, we investigate temporal and...
Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification [0.03%]
基于混合卷积和双向LSTM网络的多标签遥感图像分类中的类注意机制探索
Yuansheng Hua,Lichao Mou,Xiao Xiang Zhu
Yuansheng Hua
Aerial image classification is of great significance in the remote sensing community, and many researches have been conducted over the past few years. Among these studies, most of them focus on categorizing an image into one semantic label,...
Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake Bay region, USA [0.03%]
美国切萨皮克湾地区NLCD2011不透水层覆盖率数据的精度评价
J Wickham,N Herold,S V Stehman et al.
J Wickham et al.
The National Land Cover Database (NLCD) contains three eras (2001, 2006, 2011) of percentage urban impervious cover (%IC) at the native pixel size (30 m-×-30 m) of the Landsat Thematic Mapper satellite. These data are potentially valuable ...
Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification [0.03%]
可学习流形对齐（LeMA）：陆地覆盖和土地利用分类的半监督跨模态学习框架
Danfeng Hong,Naoto Yokoya,Nan Ge et al.
Danfeng Hong et al.
In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community-can a limited amount of highly-discriminative (e.g., hyperspectral) training data improve the performance of a ...
Hossein Bagheri,Michael Schmitt,Pablo d&#x;Angelo et al.
Hossein Bagheri et al.
Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverage, and spectral imaging ability, fusion techniques are requi...
Improving the Prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products [0.03%]
基于MODIS时序数据改进非洲稀树草原植被变量预测精度的研究
Miriam Tsalyuk,Maggi Kelly,Wayne M Getz
Miriam Tsalyuk
African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and ...