Many studies have been done to detect smokes from satellite imagery. However,
these prior methods are not still effective in detecting various smokes in
complex backgrounds. Smokes present challenges in detection due to variations
in density, color, lighting, and backgrounds such as clouds, haze, and/or mist,
as well as the contextual nature of thin smoke. This paper addresses these
challenges by proposing a new segmentation model called VTrUNet which consists
of a virtual band construction module to capture spectral patterns and a
transformer boosted UNet to capture long range contextual features. The model
takes imagery of six bands: red, green, blue, near infrared, and two shortwave
infrared bands as input. To show the advantages of the proposed model, the
paper presents extensive results for various possible model architectures
improving UNet and draws interesting conclusions including that adding more
modules to a model does not always lead to a better performance. The paper also
compares the proposed model with very recently proposed and related models for
smoke segmentation and shows that the proposed model performs the best and
makes significant improvements on prediction performances

提出了一种名为 VTrUNet 的新分割模型，通过红、绿、蓝、近红外和两个短波红外波段的图像为输入，捕捉光谱模式和长距离上下文特征，用于检测各种复杂背景下的烟雾。与其他相关模型相比，该模型在烟雾分割中表现最佳，并在预测性能上取得了显著改进。