## 1 Introduction
Different carried payloads mean different detection principles and detection capabilities, and current technologies mainly include RGB high-definition camera [20; 21] and thermal imaging camera [22] on UAV for low altitude search. But the RGB camera still appears to have insufficient resolution and low SNR when the detection distance is long or the object is similar in color with the environment, and even appears underexposure or overexposure when the ambient light changes [23]. Similarly, the thermal signal of the human body would be covered by the halos when the ambient temperature is higher than 30 \({}^{\circ}\)C.  
As an optimized form of hyperspectral technology, multispectral could relatively streamline data volume and realize real-time imaging processing by rationally selecting 4-10 characteristic spectrum bands for data processing [24], which could ensure sufficient information volume. By analyzing the differences of spectral characteristic curves between target and circumstances, specific features in different bands can be exploited to identify the target. At present, UAV-based multispectral detection technology is widely used in agricultural, forestry, and environmental monitoring under low altitude cruise conditions. Based on this technology, researchers have successfully achieved some detection performance, such as the damage assessment to rapeseed crop after winter [25], decision support system design for variable rate irrigation [26], fast Xylella symptoms detection in olive trees [27] and inferring the spatial distribution of chlorophyll concentration and turbidity in surface waters to monitor the nearshore-offshore water quality [25] and so on. However, for the rapid detection and identification of injured human subjects in an outdoor environment, the detection scenario is remarkably different from the above scenarios, showing more severe detection difficulties and challenges. Unfortunately, there is still a lack of corresponding effective technology. Fundamentally, the key difficulties lie in: (1) the injured human subject in an outdoor environment is always in a still state without remarkable motion, such as lying on the ground, making it difficult to be detected; (2) the injured human target is just a small target with a much smaller area compared with the surrounding environment under an airborne high-altitude view; (3) the difference between the clothing of injured people and the surrounding environment is weak, showing low target-background contrast; (4) additionally, all these methods cannot acquire unique physiological characteristics to identify the human attribute of the suspected target. In this paper, we propose an automatic recognition method for injured human targets in an outdoor natural environment based on UAV bimodal information. It can not only efficiently detect the suspected human subjects based on multispectral features under low target-background contrast but also acquire the vital signals for re-identification through autonomously scanning large areas using a quad-rotor UAV equipped with a multispectral imaging sensor and bio-radar. This paper will be organized as follows: The materials and methods will be introduced in Section 2, including multispectral image acquisition, multispectral image pre-processing, and target identification. Section 3 presents the results of each step mentioned above. Section 4 discusses the experiment results and conclusion.