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Found 4 papers, 2 papers with code
Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification
As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples.
Ranked #10 on
Hyperspectral Image Classification
on Pavia University
(Overall Accuracy metric)
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.
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.
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.
