A transformer boosted UNet for smoke segmentation in complex backgrounds in multispectral LandSat imagery

TL;DR

This paper introduces VTrUNet, a novel segmentation model combining spectral pattern capture and transformer-based contextual analysis, significantly improving smoke detection accuracy in complex multispectral satellite imagery.

Contribution

The paper proposes VTrUNet, a new model integrating spectral and long-range contextual features for improved smoke segmentation in challenging backgrounds.

Findings

VTrUNet outperforms recent models in smoke segmentation accuracy.

Adding more modules does not always enhance performance.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

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