Patch-U-Net: tree species classification method based on U-Net with class-balanced jigsaw resampling

ABSTRACT

Automatic tree species classification based on remote-sensing images can significantly improve the efficiency of tree species investigation and save considerable cost of human labor. A fully convolutional network (FCN) can automatically extract tree species-related features to achieve higher classification performance. However, this kind of method needs a large quantity of training data. Due to few samples and unbalanced sample distribution of tree species remote-sensing images, directly applying FCN to tree species image classification task could not achieve good results. We proposed Patch-U-Net to tackle the above problem. Our method adopts the class-balanced jigsaw resampling strategy to explicitly balance inter-class distribution and augment data in patch-wise. Besides, it extracts multi-scale information of each patch by combining the encoder–decoder and skip connection structure. We compared Patch-U-Net with six existing methods, and Patch-U-Net achieved the best performance. Specifically, Pixel Accuracy (PA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU) of Patch-U-Net are 80.33%, 57.46%, and 67.37%, which are 14.3%, 33.16%, and 17.81% higher than those of the baseline model U-Net, respectively. The results show that Patch-U-Net can improve the performance of remote-sensing tree species classification by solving the problem of unbalanced samples, which is more suitable for the remote-sensing image classification of tree species with imbalance species.

Keywords:

• remote-sensing image
• tree species classification
• fully convolution network
• class-balanced jigsaw resampling
• Patch-U-Net

Acknowledgement

We would like to express our gratitude to the Beijing Municipal Natural Science Foundation (Nos. 6214040 and 6192019) and the Fundamental Research Funds for the Central Universities (No. 2021ZY70).

Disclosure statement

The authors declare no conflicts of interest.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was jointly supported by the Beijing Municipal Natural Science Foundation [Nos. 6214040 and 6192019] and the Fundamental Research Funds for the Central Universities [No. 2021ZY70]