2024/06/18 by Jixue Liu, Liu, Jixue, Jiuyong Li +5 · 1 citation
Engineering · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fire Detection and Safety Systems #Impact of Light on Environment and Health #Infrared Target Detection Methodologies
paper · pdf · doi:10.48550/arxiv.2406.13105
openalex publication_date 2024/06/18 · openalex created_date 2024/06/22 · openalex updated_date 2026/07/28
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