This paper is focused on deriving an optimal image smoother. The optimization is done through the minimization of the norm of the Laplace operator in the image coordinate system. Discretizing the Laplace operator and using the method of Euler-Lagrange result in a weighted average scheme for the optimal smoother. Satellite imagery can be smoothed by this optimal smoother. It is also very fast and can be used for detecting the anomalies in the image. A real anomaly detecting problem is considered for the Qom region in Iran. Satellite image in different bands are smoothed. Comparing the smoothed and original images in different bands, the maps of anomalies are presented. Comparison between the derived method and the existing methods reveals that it is more efficient in detecting anomalies in the region.
In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to the powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden in the latent space are not discovered by exploiting the reconstruction error because the probability distribution of anomalies is not explicitly modeled. To address the issue, we propose a novel probability distribution representation detector (PDRD) that explores the intrinsic distribution of both the background an…
Anomaly detection in crowd videos has become a popular area of research for the computer vision community. Several existing methods generally perform a prior training about the scene with or without the use of labeled data. However, it is difficult to always guarantee the availability of prior data, especially, for scenarios like remote area surveillance. To address such challenge, we propose an adaptive training-less system capable of detecting anomaly on-the-fly while dynamically estimating and adjusting response based on certain parameters. This makes our system both training-less and adapt…
Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace. Turbine blades are susceptible to both operational and weather-based damage over time, reducing the energy efficiency output of turbines. In this study, we address automating the otherwise time-consuming task of both blade detection and extraction, together with fault detection within UAV-captured turbine blade inspection imagery. We propose BladeNet, an applicati…
Camera-based physiological monitoring, especially remote photoplethysmography (rPPG), is a promising tool for health diagnostics, and state-of-the-art pulse estimators have shown impressive performance on benchmark datasets. We argue that evaluations of modern solutions may be incomplete, as we uncover failure cases for videos without a live person, or in the presence of severe noise. We demonstrate that spatiotemporal deep learning models trained only with live samples "hallucinate" a genuine-shaped pulse on anomalous and noisy videos, which may have negative consequences when rPPG models are…
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）
Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization
School of Mathematics and Statistics, Southwest University（数学与统计学学院，西南大学）；School of Mathematics and Statistics, Xi’an Jiaotong University（数学与统计学学院，西安交通大学）；School of Computer Science and Technology and Ministry of Education Key Lab For Intelligent Networks and Network Security, Xi’an Jiaotong University（计算机科学与技术学院和教育部智能网络与网络安全重点实验室，西安交通大学）；School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology of China（数学科学学院/图像与视觉计算研究中心，电子科技大学）；Institute of Methodologies for Environmental Analysis, CNR-IMAA（环境分析方法学研究所，CNR-IMAA）
In this paper a new method of image smoothing for satellite imagery and its applications in environmental remote sensing are presented. This method is based on the global gradient minimization over the whole image. With respect to the image discrete identity, the continuous minimization problem is discretized. Using the finite difference numerical method of differentiation, a simple yet efficient 5*5-pixel template is derived. Convolution of the derived template with the image in different bands results in the discrimination of various image elements. This method is extremely fast, besides bei…
Polarimetric Synthetic Aperture Radar (PolSAR) images are establishing as an important source of information in remote sensing applications. The most complete format this type of imaging produces consists of complex-valued Hermitian matrices in every image coordinate and, as such, their visualization is challenging. They also suffer from speckle noise which reduces the signal-to-noise ratio. Smoothing techniques have been proposed in the literature aiming at preserving different features and, analogously, projections from the cone of Hermitian positive matrices to different color representatio…
Anomaly detection is a field of intense research. Identifying low probability events in data/images is a challenging problem given the high-dimensionality of the data, especially when no (or little) information about the anomaly is available a priori. While plenty of methods are available, the vast majority of them do not scale well to large datasets and require the choice of some (very often critical) hyperparameters. Therefore, unsupervised and computationally efficient detection methods become strictly necessary. We propose an unsupervised method for detecting anomalies and changes in remot…
Dynamic mapping from static labels: remote sensing dynamic sample generation with temporal-spectral embedding
Department of Geography, The University of Hong Kong, Hong Kong, China（香港大学地理系）；Pengcheng Laboratory, Shenzhen, China（鹏城实验室）；Department of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China（哈尔滨工业大学（深圳）电子与信息工程学院）
Disentangling Hardness from Noise: An Uncertainty-Driven Model-Agnostic Framework for Long-Tailed Remote Sensing Classification
Research Center for Space Computing System, Zhejiang Lab（空间计算系统研究中心，浙江实验室）；School of Cyber Science and Engineering, Zhengzhou University（网络安全工程学院，郑州大学）；School of Computer Science and Technology, Xi'an Jiaotong University（计算机科学与技术学院，西安交通大学）
CRISP: Calibration-Aware Visual State Space Duality for Remote Sensing Semantic Segmentation
Tianmushan Laboratory, Beihang University（北京航空航天大学天目山实验室）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；School of Astronautics, Beihang University（北京航空航天大学宇航学院）
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；School of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）；College of Urban and Environment Sciences, Central China Normal University（华中师范大学城市与环境科学学院）
Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment
Southwest Jiaotong University（西南交通大学）；Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education（教育部可持续城市智能交通工程研究中心）；Carnegie Mellon University（卡内基梅隆大学）
6thGrid-Net: Unified Remote Sensing Image Dehazing Based on Color Restoration and Edge-Preserving
Harbin Institute of Technology（哈尔滨工业大学）；Nanjing University of Science and Technology（南京理工大学）；National University of Defense Technology（国防科技大学）；Shandong Normal University（山东师范大学）；Qilu University of Technology（齐鲁工业大学）
Contributions to Label-Efficient Learning in Computer Vision and Remote Sensing
Université Bretagne Sud（南布列塔尼大学）；UMR IRISA（IRISA联合研究单位）；CentraleSupélec（中央理工-高等电力学院）；INRIA（法国国家信息与自动化研究所）；Technische Universität Berlin（柏林工业大学）；Université Savoie Mont Blanc（萨瓦勃朗峰大学）；Université de Caen Normandie（卡昂诺曼底大学）
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
Dept. of Artificial Intelligence, Hanyang University（人工智能系，翰阳大学）；Dept. of Data Science, Hanyang University（数据科学系，翰阳大学）；Global Technology Research, Samsung Electronics（三星电子全球技术研究）
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；International Research Center of Big Data for Sustainable Development Goals（可持续发展大数据国际研究中心）
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite. The recent advancement and even revolution of the HS RS technique offer opportunities to realize the full potential of various applications, while confronting new challenges for efficiently processing and analyzing the enormous HS acquisition data. Due to the maintenance of the 3-D HS …
AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation
Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China（南方科技大学计算机科学与工程系，深圳，中国）；School of EEE, Nanyang Technological University (NTU), Singapore（南洋理工大学电子工程学院，新加坡）；CFAR, Agency for Science, Technology and Research (A*STAR), Singapore（科技研究局（A*STAR）的CFAR，新加坡）
Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition
State Key Laboratory of Spatial Datum, College of Remote Sensing and Geoinformatics Engineering, Henan University（空间基准国家重点实验室、遥感与地理信息工程学院、河南大学）；School of Computer and Information Engineering, Henan University（计算机与信息工程学院、河南大学）；School of Automation Science and Electrical Engineering, Beihang University（自动化科学与电气工程学院、北京航空航天大学）
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation
Technical University of Munich (TUM)（慕尼黑工业大学）；Remote Sensing Technology Institute, German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Iceland（冰岛大学）；Griffith University（格里菲斯大学）
From Remote Sensing to Multiple Time Horizons Forecasts: Transformers Model for CyanoHAB Intensity in Lake Champlain
Vermont Artificial Intelligence Laboratory (VaiL)（佛蒙特人工智能实验室（VaiL））；Department of Computer Science, University of Vermont（大学计算机科学系）；University of Vermont（佛蒙特大学）；Department of Community Development and Applied Economics & Department of Computer Science, University of Vermont（社区发展与应用经济学系及计算机科学系，佛蒙特大学）；Department of Geology, University of Vermont（地质学系，佛蒙特大学）；Department of Civil and Environmental Engineering, University of Vermont（土木与环境工程系，佛蒙特大学）；Vermont EPSCoR (Established Program to Stimulate Competitive Research), University of Vermont（佛蒙特EPSCoR（促进竞争性研究计划），佛蒙特大学）；Vermont Department of Environmental Conservation（佛蒙特州环境保护部）；Water Quality Solutions, Inc.（水质解决方案公司）
In recent years, increasing attentions are paid on object detection in remote sensing imagery. However, traditional optical detection is highly susceptible to illumination and weather anomaly. It is a challenge to effectively utilize the cross-modality information from multi-source remote sensing images, especially from optical and synthetic aperture radar images, to achieve all-day and all-weather detection with high accuracy and speed. Towards this end, a fast multi-source fusion detection framework is proposed in current paper. A novel distance-decay intersection over union is employed to e…
Latent Space Guided Scenario Sampling for Multimodal Segmentation Under Missing Modalities
organization= Department of Computer Engineering, Ankara University, Ankara, T \"u rkiye；organization= Department of Modeling；Simulation, Graduate School of Informatics, METU, Ankara, T \"u rkiye
Image segmentation is a fundamental step for the interpretation of Remote Sensing Images. Clustering or segmentation methods usually precede the classification task and are used as support tools for manual labeling. The most common algorithms, such as k-means, mean-shift, and MRS, require an extra manual step to find the scale parameter. The segmentation results are severely affected if the parameters are not correctly tuned and diverge from the optimal values. Additionally, the search for the optimal scale is a costly task, as it requires a comprehensive hyper-parameter search. This paper pro…
