Patch-U-Net: tree species classification method based on U-Net with class-balanced jigsaw resampling. Issue 2 (17th January 2022)

• Record Type: Journal Article
• Authors: Qi, Tao; Zhu, Haowei; Zhang, Junguo; Yang, Zihe; Chai, Lei; Xie, Jiangjian
• Is Part Of: International journal of remote sensing. Volume 43:Issue 2(2022)
• Page Start: 532
• Page End: 548
• Publication Date: 2022-01-17
• Subjects: remote-sensing image -- tree species classification -- fully convolution network -- class-balanced jigsaw resampling -- Patch-U-Net; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.2019850
• Languages: English
• ISSNs: 0143-1161

Abstract

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.