An improved categorical cross entropy for remote sensing image classification based on noisy labels

作者：

Highlights：

• This paper addresses the noisy label issue in remote sensing images classification.

• An improved categorical cross entropy (ICCE) is proposed to handle noisy labels.

• The theoretical error bound of ICCE is given with strict mathematical proof.

• We demonstrate the effectiveness of ICCE on three remote sensing image datasets.

摘要

•This paper addresses the noisy label issue in remote sensing images classification.•An improved categorical cross entropy (ICCE) is proposed to handle noisy labels.•The theoretical error bound of ICCE is given with strict mathematical proof.•We demonstrate the effectiveness of ICCE on three remote sensing image datasets.

论文关键词：Volunteered geographic information,Remote sensing image classification,Deep convolutional neural networks,Noisy label,Improved categorical cross-entropy,Sample weighting scheme

论文评审过程：Received 2 August 2021, Revised 18 October 2021, Accepted 22 April 2022, Available online 18 May 2022, Version of Record 2 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117296