*5.2. Importance Evaluation of the Observation Angle and Band Selection*

Figure 14 shows the variation in the classification accuracy (overall accuracy and kappa coefficient) based on a single observation angle feature in the main observation plane and main vertical plane. The angle feature in the main plane was observed. The angle feature located in the backward reflection direction (zenith angle between −10◦ and −20◦) was associated with the optimal overall accuracy and kappa coefficient. In the main vertical observation plane, the classification accuracy exhibited a

symmetrical phenomenon with the angle distribution, and the variation in amplitude was lower than that in the main observation plane.

**Figure 14.** Observation angle importance analysis for the main plane. The blue/red dotted lines represent the classification results using DOM data.

Hyperspectral remote sensing has the advantage of providing hundreds of spectral channels of data to obtain the spectral curves to reflect the attribute differences of the object. It also provides convenience for the study of the band sensitivity of different vegetation types. Figure 15 shows the top 10 bands in terms of feature importance when only the multiband dataset for the observation zenith angle was used for classification in the main observation plane, where the diameter of the circle represents the importance degree of the band. The importance of features were calculated using SPSS Clementine software, and the indicators included the sensitivity and information gain contribution. The results show that in the main plane of observation, the blue band (466–492 nm), green band (494–570 nm), red band (642–690 nm), red edge band (694–774 nm), and near-infrared band (810–882 nm) were of high importance, among which the blue light band, red light band, and red edge band were the most important.

**Figure 15.** The importance of band selection at each angle in the principal plane. The diameter of the circle represents the importance of the band.

## **6. Conclusions**

In this paper, the application of UAV multi-angle remote sensing in the fine classification of vegetation was studied by combining a constructed multi-angle remote sensing BRDF model with an object-oriented classification method. High-resolution image classification extraction with a UAV was the objective, and the importance of ground object BRDF characteristic parameters was discussed in detail. In addition, considering the spectral segmentation advantage of hyperspectral data and the importance of features from the two principal planes, the observation angles and band conditions of the participating classifications were further analyzed. The main conclusions are as follows.

(1) The overall classification accuracy (63.9%) based on the BRDF vertical observation reflectance characteristics was approximately 24% higher than that of traditional UAV orthophoto-based classification. The combined application of the reflection features from the main observation plane and main vertical plane yielded the best classification results, with an overall accuracy of approximately 89.2% and a kappa of 0.870.

(2) The reflectance characteristics near the hot spots were favorable for distinguishing between corn, soybean, and weeds. The combined application of the reflectance characteristics from the main observed plane could improve the classification accuracy of trees with different leaf shapes.

(3) The viewing angle characteristics in the retroreflective direction of the principal plane were better than those in the forward reflection direction. The observation angles associated with zenith angles between −10◦ and −20◦ were the most favorable for vegetation classification (sun position: zenith angle 28.86◦, azimuth 169.07◦).

(4) Bands of high importance for the fine classification of vegetation included the blue band (466–nm), green band (494–570 nm), red band (642–690 nm), red edge band (694–774 nm), and near-infrared band (810–882 nm), among which the blue, red, and red edge bands were the most important.

Due to the UAV hyperspectral image with a centimeter spatial resolution, when the research target size was larger than the image resolution, the introduction of an object-oriented analysis method can make the work of target recognition more accurate and efficient. Additionally, combining the construction of a multi-angle remote sensing BRDF model with an object-oriented classification method is very conducive to the study of the BRDF characteristics of canopy level vegetation. The research results provide a methodological reference and technical support for BRDF construction based on UAV multi-angle measurements, which promotes the development of multi-angle remote sensing technology in vegetation information extraction. The study provides important theoretical significance and application value for regional to global vegetation remote sensing applications. In this paper, only two classification characteristics of the reflectance and model parameters were proposed for the BRDF model. Research on the application of index characteristics, such as the vegetation index and BRDF shape index in vegetation classification, along with an evaluation of different classifiers, will be developed in future work.

**Author Contributions:** Conceptualization, L.D.; methodology, L.D. and Y.Y.; software, L.D. and Y.Y.; validation, Y.Y.; formal analysis, Y.Y.; investigation, L.D. and Y.Y.; resources, L.D.; data curation, L.D.; writing—original draft preparation, Y.Y.; writing—review and editing, L.D., X.L. and L.Z.; supervision, X.L. and L.Z.; project administration, L.D.

**Funding:** This research and APC was funded by National Key R&D Program of China, grant number no. 2018YFC0706004.

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