• Corpus ID: 244800748

Multi-Label Noise Robust Collaborative Learning Method for Remote Sensing Image Classification

@inproceedings{Aksoy2020MultiLabelNR,
  title={Multi-Label Noise Robust Collaborative Learning Method for Remote Sensing Image Classification},
  author={Ahmet Aksoy and Mahdyar Ravanbakhsh and Beg{\"u}m Demir},
  year={2020}
}
—The development of accurate methods for multi- label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on Convolutional Neural Networks (CNNs) have shown strong performance gains in RS. However, they usually require a high number of reliable training images annotated with multiple land-cover class labels. Collecting such data is time- consuming and costly. To address this problem, the publicly available thematic products… 
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification
TLDR
Three different noise robust CV SLC methods are investigated and adapted to be robust for multi-label noise scenarios in RS and some guidelines for a proper design of label-noise robust MLC methods is derived.

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