Papers › Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification

Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification

22 Jul 2018IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018 7archive 2025-07-28

Sheng-Jie Liu, Haowen Luo, Ying Tu, Zhi He, Jun Li

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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