부산대학교 도서관

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite has been used for the early detection and daily monitoring of active wildfires. How to effectively segment the active fire (AF) pixels from VIIRS image time-series in a reliable manner remains a challenge because of the low precision associated with high recall using automatic methods. For AF detection, multicriteria thresholding is often applied to both low-resolution and mid-resolution Earth observation images. Deep learning approaches based on convolutional neural networks (ConvNets) are also well-studied on mid-resolution images. However, ConvNet-based approaches have poor performance on low-resolution images because of the coarse spatial features. On the other hand, the high temporal resolution of VIIRS images highlights the potential of using sequential models for AF detection. Transformer networks, a recent deep learning architecture based on self-attention, offer hope as they have shown strong performance on image segmentation and sequential modeling tasks within computer vision. In this research, we propose a transformer-based solution to segment AF pixels from the VIIRS time-series. The solution feeds a time-series of tokenized pixels into a transformer network to identify AF pixels at each timestamp and achieves a significantly higher F1-score than prior approaches for AFs within the study areas in California, New Mexico, and Oregon in the U.S., and in British Columbia and Alberta in Canada, as well as in Australia, and Sweden.