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Found 7 papers, 3 papers with code
DSAMNet: A Deeply Supervised Attention Metric Based Network for Change Detection of High-Resolution Images
In view of the insufficient of current change detection, we propose a deeply-supervised attention metric-based network (DSAMNet) for bi-temporal image change detection.
Improving Global Forest Mapping by Semi-automatic Sample Labeling with Deep Learning on Google Earth Images
To provide guidelines for users and producers, it is urgent to produce a validation sample set at the global level.
Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions
The experimental results demonstrate the superiority of the proposed method, which not only outperforms all baselines -with the highest F1 scores of 87. 40% on the building change detection dataset and 92. 94% on the change detection dataset -but also obtains the best accuracies on experiments performed with images having a 4x and 8x resolution difference.
EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles
After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas.
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning
Current hyperspectral image classification assumes that a predefined classification system is closed and complete, and there are no unknown or novel classes in the unseen data.
Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification
In this letter, we take the advantage of active learning and propose active ensemble deep learning (AEDL) for PolSAR image classification.
Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification
Deep learning models have achieved promising results on hyperspectral image classification, but their performance highly rely on sufficient labeled samples, which are scarce on hyperspectral images.
