Observing Individuals and Behavior of Hainan Gibbons (Nomascus hainanus) Using Drone Infrared and Visible Image Fusion Technology

Abstract

The Hainan gibbon (Nomascus hainanus) is one of the most endangered primates in the world. Infrared and visible images taken by drones are an important and effective way to observe Hainan gibbons. However, a single infrared or visible image cannot simultaneously observe the movement tracks of Hainan gibbons and the appearance of the rainforest. The fusion of infrared and visible images of the same scene aims to generate a composite image which can provide a more comprehensive description of the scene. We propose a fusion method of infrared and visible images of the Hainan gibbon for the first time, termed Swin-UetFuse. The Swin-UetFuse has a powerful global and long-range semantic information extraction capability, which is very suitable for application in complex tropical rainforest environments. Firstly, the hierarchical Swin Transformer is applied as the encoder to extract the features of different scales of infrared and visible images. Secondly, the features of different scales are fused through the l1-norm strategy. Finally, the Swing Transformer blocks and patch-expanding layers are utilized as the decoder to up-sample the fusion features to obtain the fused image. We used 21 pairs of Hainan gibbon datasets to perform experiments, and the experimental results demonstrate that the proposed method achieves excellent fusion performance. The infrared and visible image fusion technology of drones provides an important reference for the observation and protection of the Hainan gibbons.