Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification
In this paper, we propose a wide contextual residual network (WCRN) with active learning (AL) for remote sensing image (RSI) classification. Although ResNets have achieved great success in various applications (e.g. RSI classification), its performance is limited by the requirement of abundant labeled samples. 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. Specifically, we first design a wide contextual residual network for RSI classification. We then integrate it with AL to achieve good machine generalization with limited number of training sampling. Experimental results on the University of Pavia and Flevoland datasets demonstrate that the proposed WCRN with AL can significantly reduce the needs of samples.
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Datasets
Pavia University
Results from the Paper
Ranked #10 on
Hyperspectral Image Classification
on Pavia University
(Overall Accuracy metric)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Hyperspectral Image Classification | Pavia University | WCRN | Overall Accuracy | 99.43% | # 10 |
