*3.3. Feature Set Construction Based on the BRDF*

To evaluate the application value of the BRDF model for vegetation classification, this study extracted two types of features from the BRDF model as the basic attributes for the identification of vegetation species. The first type was bidirectional reflectance factor (BRF) predicted by the BRDF model, including the maximum (hot spot) and the minimum (dark spot) reflectance values observed in the backscattering and forward scattering regions, respectively; the multi-angle observation reflectance in the main plane of the observation (considering the maximum view zenith angle of the remote sensing sensor, which was set to 60◦); and then the observations in the principal planes beginning from the 0◦ zenith angle in the forward and backward directions of observation with a 10◦ sampling interval to obtain the multi-angle observation data. The multi-angle observed reflectance of the main vertical observation plane (the angular sampling method was consistent with the main plane of observation) and joint feature set of multi-angle reflectance for the main planes (25) were also considered. Second, the BRDF model parameters *fiso*, *fvol*, and *fgeo* were considered [25]. Table 2 summarizes the feature sets used for vegetation species identification.

**Table 2.** Feature set construction using the BRDF for object-oriented classification.


#### *3.4. Vegetation Classification and Accuracy Assessment*

After obtaining the noise attribute information for each object according to the above scheme, the C5.0 decision tree [32] method was used to construct the vegetation species recognition model. The decision tree algorithm has a structure similar to the tree structure shown in the flow chart. This structure can intuitively display the classification rules, and the classification algorithm has a fast speed, high accuracy, and simple generation mode. This study used the SPSS Clementine V16.0 software (IBM, Chicago, USA) to achieve a fine classification of vegetation based on the C5.0 decision tree. To verify the effectiveness of the method, the image segmentation results were taken as samples, and the number of each sample was summarized, as shown in Table 3. Sixty percent of the samples were used as model training samples, and the remaining 40% were used as verification samples.

**Table 3.** Samples of vegetation types.


The quantitative evaluation of the classification results mainly included the following index factors [33]: confusion matrix (overall accuracy, producer's accuracy, and user's accuracy) and the kappa coefficient. The overall accuracy is essentially tells us out of all the reference sites, what proportion were mapped correctly. The producer's accuracy is the map accuracy from the point of view of the map maker (the producer). This is how often real features on the ground are correctly shown on the classified map or the probability that a certain land cover of an area on the ground is classified as such. The user's accuracy is the accuracy from the point of view of a map user, not the map maker. It essentially tells the user how often the class on the map will actually be present on the ground. This is referred to as the reliability. The kappa coefficient is a statistical measure of inter-rater agreement or inter-annotator agreement for qualitative (categorical) items.
