Zhao, Chunhui, Shen, Yi, Su, Nan, Yan, Yiming, Feng, Shou, Xiang, Wei, Liu, Yong, and Zhao, Tianhao (2024) A Label Correction Learning Framework for Gully Erosion Extraction Using High-Resolution Remote Sensing Images and Noisy Labels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17. pp. 1638-1655.

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (19MB) | Preview

 

1

Abstract

Rapid and reliable gully erosion (GE) extraction from high-resolution remote sensing (HRRS) images is crucial for the development of land protection measures. For this task, semantic segmentation methods are widely considered the state-of-the-art solutions. Nevertheless, providing sufficient and clean training labels for segmentation models demands substantial expenses and time investment. In this context, a label correction learning (LCL) framework is proposed to effectively extract GE from HRRS images by leveraging more readily available noisy labels. The core objective of this framework is to suppress the adverse impact of noise within noisy labels on the model performance. To achieve this, we introduce three key components in the framework, including an adaptive correction loss function, a multitree refinement module, and a noise correction module. These components collaborate to rectify noisy labels during training, thereby providing the model with a training set containing less noise. To validate the effectiveness of the LCL framework, three severely eroded regions in Northeast China are selected as study areas and corresponding noisy datasets are generated. Extensive experiments on these datasets demonstrate that our framework can significantly mitigate the negative influence of label noise and ultimately achieve superior GE extraction performance. Moreover, by employing the proposed framework, we generate GE coverage maps for the study areas and obtain measurements of gully area and length that are very close to the true statistics. Such a framework that can effectively learn from noisy labels holds promise as a practical and cost-efficient means to provide reliable data references for land resource protection.

Actions (Repository Staff Only)

Item Control Page