Aerial scene understanding in the wild: Multi-scene recognition via prototype-based memory networks [0.03%]
野外天空场景识别：基于原型的内存网络实现多场景识别方法
Yuansheng Hua,Lichao Mou,Jianzhe Lin et al.
Yuansheng Hua et al.
Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while ...
Empirical validation of photon recollision probability in single crowns of tree seedlings [0.03%]
幼苗单冠中光子二次碰撞概率的经验验证
Aarne Hovi,Petri Forsström,Giulia Ghielmetti et al.
Aarne Hovi et al.
Physically-based methods in remote sensing provide benefits over statistical approaches in monitoring biophysical characteristics of vegetation. However, physically-based models still demand large computational resources and often require r...
Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area [0.03%]
基于GIS的城区单幅SAR图像自动配准方法研究
Yao Sun,Sina Montazeri,Yuanyuan Wang et al.
Yao Sun et al.
Existing techniques of 3-D reconstruction of buildings from SAR images are mostly based on multibaseline SAR interferometry, such as PSI and SAR tomography (TomoSAR). However, these techniques require tens of images for a reliable reconstru...
X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data [0.03%]
X-ModalNet:用于遥感数据分类的半监督深度跨模态网络
Danfeng Hong,Naoto Yokoya,Gui-Song Xia et al.
Danfeng Hong et al.
This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture rada...
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion [0.03%]
基于深度残差神经网络和SAR-光学数据融合的Sentinel-2影像云去除研究
Andrea Meraner,Patrick Ebel,Xiao Xiang Zhu et al.
Andrea Meraner et al.
Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessm...
Deep Gaussian processes for biogeophysical parameter retrieval and model inversion [0.03%]
用于生物地球物理参数检索和模型反演的深度高斯过程
Daniel Heestermans Svendsen,Pablo Morales-Álvarez,Ana Belen Ruescas et al.
Daniel Heestermans Svendsen et al.
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with...
Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations [0.03%]
基于长时间序列的NOAA-AVHRR和MODIS数据监测全球作物年际产量变化
Xiaoyang Zhang,Qingyuan Zhang
Xiaoyang Zhang
Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This s...
Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine [0.03%]
基于2018年时序Landsat影像和Google Earth Engine的中国沿海湿地制图研究
Xinxin Wang,Xiangming Xiao,Zhenhua Zou et al.
Xinxin Wang et al.
Coastal wetlands, composed of coastal vegetation and non-vegetated tidal flats, play critical roles in biodiversity conservation, food production, and the global economy. Coastal wetlands in China are changing quickly due to land reclamatio...
A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks [0.03%]
基于全卷积神经网络的Sentinel-2影像全球建成环境制图框架研究
Chunping Qiu,Michael Schmitt,Christian Geiß et al.
Chunping Qiu et al.
Human settlement extent (HSE) information is a valuable indicator of world-wide urbanization as well as the resulting human pressure on the natural environment. Therefore, mapping HSE is critical for various environmental issues at local, r...
Relative space-based GIS data model to analyze the group dynamics of moving objects [0.03%]
基于相对空间的GIS数据模型及其群体运动对象的动力学分析方法研究
Mingxiang Feng,Shih-Lung Shaw,Zhixiang Fang et al.
Mingxiang Feng et al.
The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in...