This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll content from multispectral data, vegetation parameters (chlorophyll, leaf area index, and fractional vegetation cover) from hyperspectral data, and in the detection of the causal direction in a collection of 28 bivariate geoscience and remote sensing causal problems. The model consistently performs better than the standard GP and the more advanced heteroscedastic GP model, both in terms of accuracy and more sensible confidence intervals.
Image Processing Laboratory (IPL)（图像处理实验室（IPL））；Universitat de València（瓦伦西亚大学）
Global warming is leading to unprecedented changes in our planet, with great societal, economical and environmental implications, especially with the growing demand of biofuels and food. Assessing the impact of climate on vegetation is of pressing need. We approached the attribution problem with a novel nonlinear Granger causal (GC) methodology and used a large data archive of remote sensing satellite products, environmental and climatic variables spatio-temporally gridded over more than 30 years. We generalize kernel Granger causality by considering the variables cross-relations explicitly in…
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
Johns Hopkins University（约翰斯·霍普金斯大学）；Data Science and AI Institute, Johns Hopkins University（约翰斯·霍普金斯大学数据科学与人工智能学院）
Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, PR China（合肥工业大学计算机科学与信息工程学院）；College of Aviation Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, PR China（中国民航飞行大学航空电子电气工程学院）；School of Computing and Information Technology, Great Bay University, Dongguan 523000, PR China（广东大湾区大学计算机科学与信息工程学院）
Universitat de València（瓦伦西亚大学）；University of Oxford（牛津大学）；German Aerospace Center（德国航空航天中心）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the in…
PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement
Great Bay University（大湾大学）；Hefei University of Technology（合肥工业大学）；Macao Polytechnic University（澳门 polytechnic 大学）；University of Science and Technology Beijing（北京科技大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳分校）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞智能与信息科技重点实验室）
Freq-RemoteVAR: Next-Frequency Autoregressive Modeling for Remote Sensing Change Detection
Zhejiang Lab（之江实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Changsha University of Science and Technology（长沙理工大学）；The Education University of Hong Kong（香港教育大学）；South China Agricultural University（华南农业大学）
SkyNative: A Native Multimodal Architecture for Remote Sensing Vision-Language Understanding
College of Computer Science and Technology, Jilin University, China（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）
Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；University of Campinas（坎皮纳斯大学）；SAMSUNG Research Institute Brazil（三星巴西研究院）；University of Feira de Santana（费拉德桑塔纳州立大学）
Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure towards flood monitoring is based on identifying the area most vulnerable to flooding, which gives authorities relevant regions to focus. In this work, we propose several methods to perform flooding identification in high-resolution remote sensing images using deep learning. Specifically, some proposed techniques are based upon unique networks, such as dilated and deconvolutional ones, while other was conceived to exploit diversity of distinct net…
