## 4 Experiment and Results
### Preliminary Detection of Suspected Human Targets  
(1) Suspected human target detection in _Scenario 1_  
Based on the multispectral sensor module, each exposure would acquire six bands of remote sensing images (a certain area under the field of view for one exposure) as shown in Figure 11. We can notice that the DN value of each band image is different because different subjects have different spectral reflectance for different bands. In addition, we can clearly observe the vignetting effect in the band six image that we must correct to achieve accurate results. To facilitate data analysis, it is necessary to preprocess the original multispectral images. After the preprocessing on multiple sets of images from multiple exposures, including image registration and radiometric calibration, and so on, a complete reflectance panorama is formed as shown in Figure 12. Although we can visually find some suspected target points, they need to be identified automatically and reasonably. Especially, when the search range is large, it is impossible to find and locate the target quickly and accurately rely on the human eye. Additionally, human subjectivity and visual fatigue have a great impact on the results. Figure 11: Local multispectral remote sensing images in _Scenario 1_ acquired by MS600 (one exposure): (**a**) 450 nm; (**b**) 555 nm; (**c**) 660 nm; (**d**) 710 nm; (**e**) 840 nm; (**f**) 940 nm. Figure 10: Illustration of experimental settings for two scenarios. (**a**) _Scenario 1_: 12 sets of green camouflage clothes in a homogeneous grassland background, (**b**) 11 sets of green camouflage clothes, and one real personnel in green camouflage clothing in a complex background. Next, the selected multispectral indexes and features in this study were fed to the decision mechanism and applied to preliminarily identify and extract suspected human targets automatically by setting a reasonable threshold. As the suspected target recognition result shown in Figure 13, the red targets mean suspected targets. Obviously, 13 remarkable suspected targets (corresponding to 13 red parts) were detected, including one undesired recognition mistake and 12 non-human fake targets (camouflage). Results show that this detection method based on multispectral feature information can effectively detect suspected targets in this outdoor environment with a relatively single background. Moreover, we also compared the classification or recognition performance of the DT with the state-of-art machine learning algorithms including Back Propagation neural network (BP), Random Forest (RF), and Support Vector Machine (SVM) with a radial basis kernel function. All these four classifiers are trained based on the same training dataset, and the preprocessed image of one exposure marked in the red dashed box in Figure 12 is taken as the test data. The classification results are as shown in Table 3. Obviously, based on the same training data and test data, the recognition performance of different classifiers does not differ greatly. It means that spectral features are the main factor affecting the recognition results to some extent. Figure 12: The complete reflectance panorama in _Scenario 1_. Figure 13: Suspected target recognition result in _Scenario 1_ (_green for vegetation, yellow for soil, red for suspected target, blue for noise spot_). (2)  
Suspected human target detection in _Scenario 2_  
_Scenario 2_ is comprehensive and more representative for practical applications. The background consists of many components with different colors (shrubs, green lawns, trees, dark yellow, and other vegetation). The detection targets (eleven pieces of camouflage and one real person in desert camouflage to disguise the human subject) contain three types of color attributes and they were placed in different vegetation areas of similar colors. Since the above analysis has demonstrated that the impact of different classifiers is not significant, here we just chose DT as the representative classification algorithm in the subsequent study.