Global2Salient: Self-adaptive feature aggregation for remote sensing smoke detection

摘要

Remote sensing (RS) images have been widely used in disaster monitoring due to their wide observation and timeliness. Wildfire is a type of destructive disaster, and smoke is an important signal of the occurrence of wildfires; therefore, it is necessary to perform smoke detection in RS images. Smoke-like scenes such as clouds captured by satellites make RS smoke detection a tough task. Differences in resolution and the complexity of the geographical environment further increases the difficulty of detection: smoke in some images only occupies a small area, while in other images it may fill the whole image. Thus, global information and salient features are both essential for RS smoke detection. From this point of view, we design a self-adaptive feature aggregation (SAFA) network to distinguish smoke from other scenes in RS images. SAFA has two pathways: the global information extraction pathway (GIEP) for capturing global information and the salient feature extraction pathway (SFEP) for extracting salient features. The self adaptive aggregation means GIEP and SFEP are summed up by trainable weight coefficients to make the final prediction. We conducted experiments on USTC_SmokeRS data, which are specially set up for RS smoke detection. This dataset contains smoke and other smoke-like scenes such as clouds, dust and haze. The experimental results show that SAFA achieves the new state-of-the-art classification accuracy of 96.22% on this dataset. (c) 2021 Elsevier B.V. All rights reserved.