## 3 Bimodal-Information-Based Human Targets Recognition Method
### Suspected Target Detection Based on Multispectral Feature Information  
(1) Multispectral image preprocessing  
Before further analyzing and processing the multispectral images, it is necessary to preprocess them to remove multifactor interference effects. The preprocessing process is shown in Figure 6.  
1 st step: Radiometric calibration. Radiometric calibration consists of radiometric correction and reflectance calculation. Radiometric correction mainly converts the DN value into spectral radiance value and corrects radiation distortion caused by lens transmission and sensor response [31]. The calculated reflectivity can reflect the real spectral characteristics of ground objects. We should identify and calculate the average radiance of the gray board pixels from gray board images, which were obtained by MS600 before the flight. At the first step, identify the gray board area in the images by selecting the largest nonzero connected region after running the function of binarization and image closure. Then, locate the center of the gray board using the average values of row coordinates and column coordinates. Make a square mask centered on the center coordinate point. The DN value of the gray board area can be extracted by dot multiplication of mask and gray board image. Finally, we can calculate the radiance of each pixel. With the above steps, the DN value of the multispectral images was converted into the reflectivity value, and the vignetting effect was corrected. 2 nd step: Image registration. Each exposure of the MS600 camera can generate six single-band images. Although this kind of sensor has the advantages of simple design  
Figure 5: The flowchart of bimodal-information-based human targets recognition method. Figure 6: Flow chart of multispectral imagery preprocessing. and low cost, the limitation of the structure will lead to a dislocation between imaging bands. Image registration must be done before further processing. In this stage, the main task is to synthesize these six single-band images into one data file in TIFF format. Firstly, the SURF (Speeded Up Robust Features) algorithm is used to detect the feature points in every image, which is a robust local feature point detection and description algorithm [32]. The Hessian matrix is constructed to generate all interest points, build scale space, locate feature points, and generate feature point descriptors. Then, after matching the feature points between every two images, the RANSAC (random sample consensus) algorithm is used to eliminate mismatching points [33]. Finally, abnormal matching points would be eliminated by computing the affine transformation matrix between each image and the registration will be completed through the affine transform. Multispectral feature extraction  
The regions of interest (ROI) can be extracted manually according to the low altitude high-definition images, which can show the distribution of experimental subjects (3 typical objects are selected, including human subjects, vegetation, and soil). The reflectivity feature is the top preferred feature. Here, a statistic experiment on the reflectivity feature distribution of different targets is carried out based on numerous collected data. The statistical result of the reflectivity of 3 ground objects in the image analyzed by ENVI software is shown in Figure 7a. It is obvious reflectivity features of six bands could be exploited to distinguish between target and background to some extent but cannot solve the problem entirely. The spectral index can be another kind of feature to enhance target difference and quantify it by band math.