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New framework enhances remote sensing image fusion with frequency-independent feature learning

Tech Xplore
A research team led by Prof. Xie Chengjun and Zhang Jie from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences...
2 weeks ago

Unsupervised domain adaptation for remote sensing semantic segmentation with the 2D discrete wavelet transform

Nature
There would be the differences in spectra, scale and resolution between the Remote Sensing datasets of the source and target domains,...
1 month ago

Deep learning-based semantic segmentation of remote sensing images: a review

Frontiers
Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial...
4 months ago

(PDF) Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data

ResearchGate
In this paper, we propose a Contrastive learning based MultiModal Alignment Network (CoMMANet) to align data from different sensors into a shared and...
2 months ago

From sensor fusion to knowledge distillation in collaborative LIBS and hyperspectral imaging for mineral identification

Nature
Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques...
7 months ago

(PDF) MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-wave Radar and Machine Vision

ResearchGate
A multi-data source deep learning object detection network (MS-YOLO) based on millimeter-wave radar and vision fusion was proposed.
1 month ago

Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing

Frontiers
In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three...
9 months ago

Multiscale knowledge distillation with attention based fusion for robust human activity recognition

Nature
Knowledge distillation is an effective approach for training robust multi-modal machine learning models when synchronous multimodal data are unavailable.
5 months ago

Machine Learning for Tactile Perception: Advancements, Challenges, and Opportunities

Wiley Online Library
Tactile sensors have developed rapidly, which increases the need for data processing. While machine learning, rather than conventional...
20 months ago

Trento: From top to bottom, the LiDAR-derived DSM image, the false...

ResearchGate
In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data sources.
9 months ago