**5. Conclusions**

Constant leaf sprouting and development can be an indication of healthy trees in beneficial environmental circumstances. This paper investigated the feasibility of NGL detection using hyperspectral detection algorithms in UAV bitmap images. Since the bitmap images only provide RGB values, using a traditional subpixel detector CEM presents false alarm issue. In order to address this issue, three window based CEMs are proposed in this paper. First, Subset CEM is developed to split the image into different small images, according to different regions. Second, the sliding window-base CEM was proposed to extract the RGB values around the current pixel to calculate autocorrelation matrix **S**. Third, this paper further proposed adaptive window-based CEM (ASW CEM), which can change the window size automatically according to NGL around the current pixel. ASW CEM extracts and calculates autocorrelation matrix **S**, increasing the contrast between NGL and the background, so as to highlight NGLs and to suppress the background. Last but not the least, in order to reduce the effect of the quality of the desired target selected for CEM, this paper designed OSGP to generate a stable desired target during iterations. The experimental results show that our proposed approaches can effectively reduce the errors resulting from a false alarm so as to obtain more appropriate desired target and stable results for newly grown tree leaves in UAV images.

**Acknowledgments:** The authors would like to acknowledge the support provided by projects MOST 106-2221-E-224-055 and MOST 105-2119-M-415-002 funded by the Ministry of Science and Technology, Taiwan, ROC.

**Author Contributions:** S.-Y.C. conceived and designed the algorithms and wrote the paper; C.L. analyzed the data, contributed data collection, reviewed the paper and organized the revision. C.-H.T. and S.-J.C. performed the experiments.

**Conflicts of Interest:** The authors declare no conflict of interest.
