The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 223–228, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-223-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 223–228, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-223-2022
 
30 May 2022
30 May 2022

DEEP LEARNING TRAINING WITH UNBALANCE SAMPLE DISTRIBUTION FOR REMOTE SENSING IMAGE SEGMENTATION

Y. Xu1, X. Hu1,2,3, J. Gong1, X. Huang1,4, and J. Li1 Y. Xu et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, China
  • 2Hubei Luojia Laboratory, China
  • 3Institute of Artificial Intelligence in Geomatics, Wuhan University, China
  • 4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China

Keywords: Sample balance, Remote sensing image, deep learning, High spatial and spectral resolution (HSSR)

Abstract. The intelligent interpretation of remote sensing images based on deep learning has become a hot spot with the increasing satellite images acquired due to the rapid development of aerospace technology. Sufficient and reasonable distributed samples are essential for the accuracy of deep learning. The spatial distribution of natural features is inhomogeneous in the real world. When people create sample dataset, they often collect within a certain local range, which may bring problems of unbalanced distribution of samples, including the unbalance between training dataset and validation dataset, and the unbalance among different sample categories. This long-tail distribution of samples (i.e., a few classes account for most of the data, while most classes are under-represented) can lead to bias in the training model and make it difficult to ensure accuracy.

In this paper we tried to solved the above-mentioned problem in landcover classification with high spatial and spectral resolution (HSSR) remote sensing images. We first adopted an iterative stratification method for multi-label data classification to ensure that both training dataset and validation dataset contain reasonable proportion of landcover classes. Then we proposed a weighted loss algorithm to further strengthen the learning ability of the model for rare categories. Experiments on a large volume HSSR dataset shows that with our methods the accuracy of landcover classification increased by 2%.