Papers Cloud Detection
“Cloud Detection” 태그가 달린 논문 72편 · 필터 해제
Comparison Research of Millimeter-Wave/Infrared Co-aperture Reflector Antenna Systems Based on a Specialized Film
This paper presents a novel co-aperture reflector antenna operating in millimeter-wave (MMW) and infrared (IR) for cloud detection radar. The proposed design combines a back-fed dual-reflector antenna, an IR optical refl…
Cloud DetectionRetrievalEfficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSats
We present the implementation of four FPGA-accelerated convolutional neural network (CNN) models for onboard cloud detection in resource-constrained CubeSat missions, leveraging Xilinx's Vitis AI (VAI) framework and Deep…
Cloud DetectionQuantizationFlexiMo: A Flexible Remote Sensing Foundation Model
The rapid expansion of multi-source satellite imagery drives innovation in Earth observation, opening unprecedented opportunities for Remote Sensing Foundation Models to harness diverse data. However, many existing model…
Cloud DetectionEarth ObservationLand Cover Classificationmodel+1SpecTf: Transformers Enable Data-Driven Imaging Spectroscopy Cloud Detection
Current and upcoming generations of visible-shortwave infrared (VSWIR) imaging spectrometers promise unprecedented capacity to quantify Earth System processes across the globe. However, reliable cloud screening remains a…
Cloud DetectionSatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery
Foundation models have the potential to transform the landscape of remote sensing (RS) data analysis by enabling large computer vision models to be pre-trained on vast amounts of remote sensing data. These models can the…
AllCloud DetectionSelf-Supervised LearningPGCS: Physical Law embedded Generative Cloud Synthesis in Remote Sensing Images
Data quantity and quality are both critical for information extraction and analyzation in remote sensing. However, the current remote sensing datasets often fail to meet these two requirements, for which cloud is a prima…
Cloud DetectionData AugmentationGenerative Adversarial NetworkUnleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation
The LiDAR-based 3D object detector that strikes a balance between accuracy and speed is crucial for achieving real-time perception in autonomous driving and robotic navigation systems. To enhance the accuracy of point cl…
3D Object DetectionAutonomous DrivingCloud DetectionKnowledge Distillation+4MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detection
Vehicular networks are exposed to various threats resulting from malicious attacks. These threats compromise the security and reliability of communications among road users, thereby jeopardizing road and traffic safety. …
Cloud DetectionLanguage ModelingLanguage ModellingLarge Language ModelHigh-Resolution Cloud Detection Network
The complexity of clouds, particularly in terms of texture detail at high resolutions, has not been well explored by most existing cloud detection networks. This paper introduces the High-Resolution Cloud Detection Netwo…
Cloud DetectionSemantic SegmentationCloudSense: A Model for Cloud Type Identification using Machine Learning from Radar data
The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of p…
Cloud DetectionMachine Learning in Space: Surveying the Robustness of on-board ML models to Radiation
Modern spacecraft are increasingly relying on machine learning (ML). However, physical equipment in space is subject to various natural hazards, such as radiation, which may inhibit the correct operation of computing dev…
Cloud DetectionDeep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data
This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satellites. The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Jus…
Cloud DetectionComputational EfficiencyRSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation
The development of high-resolution remote sensing satellites has provided great convenience for research work related to remote sensing. Segmentation and extraction of specific targets are essential tasks when facing the…
Cloud DetectionImage SegmentationSegmentationSemantic SegmentationBenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery
Satellites equipped with optical sensors capture high-resolution imagery, providing valuable insights into various environmental phenomena. In recent years, there has been a surge of research focused on addressing some c…
Body DetectionCloud DetectionDisaster ResponseSegmentation+1FAKEPCD: Fake Point Cloud Detection via Source Attribution
To prevent the mischievous use of synthetic (fake) point clouds produced by generative models, we pioneer the study of detecting point cloud authenticity and attributing them to their sources. We propose an attribution f…
AttributeCloud DetectionCLiSA: A Hierarchical Hybrid Transformer Model using Orthogonal Cross Attention for Satellite Image Cloud Segmentation
Clouds in optical satellite images are a major concern since their presence hinders the ability to carry accurate analysis as well as processing. Presence of clouds also affects the image tasking schedule and results in …
Cloud DetectionImage SegmentationSegmentationSemantic SegmentationCreating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI
Cloud formations often obscure optical satellite-based monitoring of the Earth's surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The int…
BenchmarkingCloud DetectionEarth ObservationDomain Adaptation for Satellite-Borne Hyperspectral Cloud Detection
The advent of satellite-borne machine learning hardware accelerators has enabled the on-board processing of payload data using machine learning techniques such as convolutional neural networks (CNN). A notable example is…
Cloud DetectionDomain AdaptationEarth ObservationTest-time AdaptationCloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of pattern recognition and classification tasks. In this work, we consider extending classic SVMs with quantum kernels and…
Cloud DetectionOptimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers. It allows to exchange highly expressive and costly circuits to moder…
Cloud Detection