## 5 Conclusions  
In this paper, we developed and validated a new shadow detection approach based on general shadow properties including high hue and low intensity in the HSV invariant color space and special spectral reflectivity features of high reflectivity values in the near infrared band and low reflectivity values in the red band for vegetation in nonshadow regions. A rough shadow image was first produced using these properties, in which shadow pixels were highlighted and nonshadow pixels were depressed. Then, the final shadow image was drawn with a certain threshold on the rough shadow image. For the validation of our method, comparative experiments were applied over three test images selected from the multispectral WorldView-3 remote sensing image of Rio de Janeiro, Brazil. The performance of our developed mixed property-based shadow detection approach was compared with other comparable shadow detection methods [11; 14; 18; 19; 20], after which the resulting experimental data were evaluated both subjectively and objectively. The resulting experimental data revealed that images produced by our method had much closer appearances to the reference images and excellent accuracy metrics, namely high average values for the overall accuracy (95.02%), the producer's accuracy (96.20%), and the specificity (92.87%), and low average values for the omitted error (3.80%) and the committed error (7.12%), when compared with those of other comparable shadow detection methods. Both the good visual sense and excellent accuracy metrics reveal that our approach makes an improvement for simultaneously further addressing the problems of shadow omission and vegetation misclassification. In the future, we will attempt to improve the shadow detection accuracy and decrease the computational time in a further step. Combined with our current work, we will simultaneously attempt to better distinguish shadows from water.  
Conceptualization, H.H., X.X., C.H. (Changhong Hu), L.H. and T.L.; Data curation, L.H., X.L. and M.W.; Formal analysis, H.H., C.H. (Chengshan Han), X.X., C.H. (Changhong Hu), X.L. and T.L.; Funding acquisition, C.H. (Chengshan Han) and X.X.; Investigation, H.H.; Methodology, H.H., X.X. and [PERSON]. ([PERSON]); Project administration, [PERSON] ([PERSON]) and X.X.; Resources, [PERSON] ([PERSON]) and X.X.; Software, H.H., L.H. and X.L.; Supervision, [PERSON] ([PERSON]) and X.X.; Validation, H.H.; Visualization, L.H., X.L. and [PERSON]; Writing--original draft, H.H.; Writing--review & editing, H.H., [PERSON] ([PERSON]), X.X. and C.H. ([PERSON]). This research was funded by the Key Project on National Defense Science and Technology Innovation of the Chinese Academy of Sciences (No. 41275487-X).  
The authors thank Dr. [PERSON] for advice on debugging programs, and Dr. [PERSON] for advice on English writing. The authors also express gratitude to Digital Global Inc. for providing the WorldView-3 image samples of Rio de Janeiro, Brazil ([[https://www.digitalglobe.com/resources/product-samples/rio-de-janeiro-brazil](https://www.digitalglobe.com/resources/product-samples/rio-de-janeiro-brazil)]([https://www.digitalglobe.com/resources/product-samples/rio-de-janeiro-brazil](https://www.digitalglobe.com/resources/product-samples/rio-de-janeiro-brazil))).  
The authors declare no conflict of interest.