Cloud Detection
22 papers with code • 0 benchmarks • 1 datasets
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Latest papers with no code
Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data
The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed.
RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation
While the direct application of SAM to remote sensing image segmentation tasks does not yield satisfactory results, we propose RSAM-Seg, which stands for Remote Sensing SAM with Semantic Segmentation, as a tailored modification of SAM for the remote sensing field and eliminates the need for manual intervention to provide prompts.
FAKEPCD: Fake Point Cloud Detection via Source Attribution
Take the open-world attribution as an example, FAKEPCD attributes point clouds to known sources with an accuracy of 0. 82-0. 98 and to unknown sources with an accuracy of 0. 73-1. 00.
CLiSA: 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.
Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection
However, prior to deployment, new missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missions will underperform when deployed to process the data on the new missions.
Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
In this work, we consider extending classic SVMs with quantum kernels and applying them to satellite data analysis.
Optimizing 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.
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images.
CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features
In the decoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extract local and global features simultaneously.
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis.