*Article* **Application of UAV-Based Multi-angle Hyperspectral Remote Sensing in Fine Vegetation Classification**

**Yanan Yan 1,2, Lei Deng 1,3,\*, XianLin Liu 1,4 and Lin Zhu 1,3**


Received: 8 October 2019; Accepted: 20 November 2019; Published: 22 November 2019

**Abstract:** To obtain a high-accuracy vegetation classification of high-resolution UAV images, in this paper, a multi-angle hyperspectral remote sensing system was built using a six-rotor UAV and a Cubert S185 frame hyperspectral sensor. The application of UAV-based multi-angle remote sensing in fine vegetation classification was studied by combining a bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods. This method can not only effectively reduce the classification phenomena that influence different objects with similar spectra, but also benefit the construction of a canopy-level BRDF. Then, the importance of the BRDF characteristic parameters are discussed in detail. The results show that the overall classification accuracy (OA) of the vertical observation reflectance based on BRDF extrapolation (BRDF\_0◦) (63.9%) was approximately 24% higher than that based on digital orthophoto maps (DOM) (39.8%), and kappa using BRDF\_0◦ was 0.573, which was higher than that using DOM (0.301); a combination of the hot spot and dark spot features, as well as model features, improved the OA and kappa to around 77% and 0.720, respectively. The reflectance features near hot spots were more conducive to distinguishing maize, soybean, and weeds than features near dark spots; the classification results obtained by combining the observation principal plane (BRDF\_PP) and on the cross-principal plane (BRDF\_CP) features were best (OA = 89.2%, kappa = 0.870), and especially, this combination could improve the distinction among different leaf-shaped trees. BRDF\_PP features performed better than BRDF\_CP features. The observation angles in the backward reflection direction of the principal plane performed better than those in the forward direction. The observation angles associated with the zenith angles between −10◦ and −20◦ were most favorable for vegetation classification (solar position: zenith angle 28.86◦, azimuth 169.07◦) (OA was around 75%–80%, kappa was around 0.700–0.790); additionally, the most frequently selected bands in the classification included the blue band (466 nm–492 nm), green band (494 nm–570 nm), red band (642 nm–690 nm), red edge band (694 nm–774 nm), and the near-infrared band (810 nm–882 nm). Overall, the research results promote the application of multi-angle remote sensing technology in vegetation information extraction and provide important theoretical significance and application value for regional and global vegetation and ecological monitoring.

**Keywords:** multi-angle observation; hyperspectral remote sensing; BRDF; vegetation classification; object-oriented segmentation

#### **1. Introduction**

The vegetation ecosystem is an important foundation for ecological systems [1]. The use of remote sensing technology has become the main approach for vegetation ecological resource surveys and environmental monitoring due to the corresponding real-time, repeatability, and wide-coverage advantages [2–4]. With the development of remote sensing technology, visible light, multispectral, hyperspectral, and other sensors have been widely used in the remote sensing of vegetation [5,6], and more hyperspectral and high-resolution information has been obtained than ever before, greatly improving the accuracy of image classification [7,8].

As one of the current frontiers of remote sensing development, hyperspectral remote sensing technology has played an increasingly important role in quantitative analyses and accurate classifications of vegetation due to its ability to acquire high-resolution spectral and spatial data [9–12]. For instance, Filippi utilized an unsupervised self-organizing neural network to perform complex vegetation mapping in a coastal wetland environment [13]. Fu et al. proposed an integrated scheme for vegetation classification by simultaneously exploiting spectral and spatial image information to improve the vegetation classification accuracy [14].

From the perspective of remote sensing imaging, remote sensing vertical photography can obtain only the spectral feature projection of the target feature in one direction, and it lacks sufficient information to infer the reflection anisotropy and spatial structure [15]. Multi-angle observations of a target can provide information in multiple directions and be used to construct the bidirectional reflectance distribution function (BRDF) [16–18], which increases the abundance of target observation information; additionally, this approach can extract more detailed and reliable spatial structure parameters than a single-direction observation can [19]. Multi-angle hyperspectral remote sensing, which combines the advantages of multi-angle observation and hyperspectral imaging technology, is projected to become an effective technical method for the classification of vegetation in remote sensing images.

The UAV remote sensing platform has emerged due to its flexibility, easy operation, high efficiency, and low cost; it can efficiently acquire high-resolution spatial and spectral data on demand [20]. The UAV remote sensing platform has the ability to provide multi-angle observations and thus has become popular in multi-angle remote sensing [21–24]. Roosjen et al. studied the hyperspectral anisotropy of barley, winter wheat, and potatoes using a drone-based imaging hyperspectrometer by obtaining multi-angle observation data for hemispherical surfaces by hovering around the crops [25]. In addition, Liu and Abd-Elrahman developed an object-based image analysis (OBIA) approach by utilizing multi-view information acquired using a digital camera mounted on a UAV [26]. They also introduced a multi-view object-based classification using deep convolutional neural network (MODe) method to process UAV images for land cover classification [27]. Both methods avoided the salt and pepper phenomenon of the classified image and have achieved favorable classification results. However, it is difficult to obtain the continuous spectrum characteristics of the ground objects because of the fewer wave bands the optical sensors use. Moreover, the research does not fully mine the contribution difference of multi-angle features. Furthermore, how to use the limited multi-angle observations to construct the BRDF of ground objects to enrich the observation information of the target is also one of the difficulties in the application of multi-angle remote sensing.

In this paper, key technical issues, such as the difficulty in distinguishing complex vegetation species from a single remote sensing observation direction, the construction of the BRDF model based on UAV multi-angle observation data, and model application for vegetation classification and extraction, were studied. The purpose of this study was to discuss the role of ground object BRDF characteristic parameters in the fine classification of vegetation, thereby improving the understanding of the relationship between the BRDF and plant leaves and vegetation canopy structure parameters, as well as promoting the application of multi-angle optical remote sensing in the acquisition of vegetation information.
