*3.1. Experimental Procedure*

This section introduces the experimental process of all detection algorithms used in this paper, including CEM, Subset CEM, Sliding Window-based CEM, and Adaptive Sliding Window-based CEM. In order to remedy the defect in the target detection algorithm that only one d can be selected at one time, the Optimal Signature Generation Process (OSGP) is used, and the optimum target of interest **d'** is iterated by iterative learning. The incorrect results of selection errors are thus reduced effectively. Figure 9 shows the approximate process of all the detection algorithms for this experiment.

**Figure 9.** Flowchart of all algorithms for this experiment.

There are two methods of hyperspectral target detection for selecting the desired target. One method only selects the single target **d** for detection. The other method selects the pixel values of multiple desired targets for detection. The CEM is the first type. CEM only selects a single target for detection, and so the quality of detection result highly depends on the selected desired target.

The desired targets **d** used by all of the detection algorithms for the experiments are randomly selected from the ground truth and **d'** is iterated by using OSGP to increase the precision of detection. The full image is used to calculate autocorrelation global **R** for target detection of CEM.

#### *3.2. Description of the Study Site*

The study site is in Baihe District (23◦20 N, 120◦27 E) which is part of Tainan City, Taiwan. Tainan City is characterized by a tropical savanna climate. The weather is generally hot and humid. The mean annual temperature is 24.38 ◦C. The authors have deployed a few permanent plots over the broadleaf forest for research of forest growth [47,48] in 2008. In which, a series of ground inventory is annually conducted for stand dynamics [49].

#### 3.2.1. UAV Data Collection

We applied the picture of a forest in the middle of Taiwan taken by a Canon PowerShot S110 camera on an eBee RTK drone flying at an altitude of 239.2 ft on 12 July 2014. This image has R, G, and B bands. Data acquisition took place under wonderful weather conditions. The ground pixel size is 6 cm, the original image resolution is 1000 × 1300 pixels, and the actual area of the full image is 60 m × 78 m. The data have been successfully used to derive forest canopy height model [50] and the desired target **d** for the target detection algorithm in this paper is the desired target that is selected randomly in the experimental image, as shown in Figure 10. The red circle is the target **d** for detection.

**Figure 10.** Study site at Baihe District, Tainan, Taiwan.
