## Materials and Methods
### Classification
#### 2.3.1 One-Dimensional Convolutional Neural Network Classifiers  
Deep learning in neural networks is the approach of composing networks into multiple layers of processing with the aim of learning multiple levels of abstraction [40]. In doing so, the network can adaptively learn low-level features from raw data and higher-level features from low-level ones in a hierarchical manner, nullifying the over-dependence of shallow networks on feature engineering [23]. The building blocks of the Conv1D used in this study are illustrated in Figure 3, including an input layer, two convolution layers with rectified linear unit (ReLU) as nonlinear activation function, a maxpool layer, a flatten layer, and two fully connected layers. The convolutional layer and pooling layer perform as hierarchical feature extractors [41], while the fully connected layer acts as a classifier that produces the predictive probabilities of all the object categories in the input data [20; 41]. According to the hyperspectral data, three features from the image were selected as inputs to the one-dimensional convolutional neural network (Conv1D), including the spectral and crown texture features (entropy and mean). Crown texture information is mainly related to crown-internal shadows, foliage properties (size, density, and reflectivity), and branching [1]. Thus, crown texture information has also been exploited to improve tree species classification [28]. Combining spectral and texture features often improves the accuracy of tree species classifications [1]. According to [PERSON] et al. [42], texture layers are known to improve the classification accuracy by up to 10%-15%. These feature data with 10,269 pixel samples are convoluted in the first layer of the model with 96 filters and a kernel size of 5 to produce feature maps in \(96\times 28\) sizes (Figure 3). Then the convolution layer is paired with a pooling layer that can be thought of as a spectral down-sampling of the convolutional feature map. A max pooling operation was utilized here and pooling layers were fixed as max-pooling with a window size of 2. This layer accepts the convolutional feature map, evaluates pairs of data elements across the spectral dimension of the feature maps, and passes the maximum value onto the next layer. This process reduces the size of the feature map while preserving the features observed within the  
Figure 2: Framework of the tree species classification based on one-dimensional convolutional neural network (Conv1D) and random forest classifier. convolutional feature map. The second layer of the model is another convolution layer with 128 filters; this layer generates new feature maps in \(28\times 128\) sizes by using the output of the previous layer. The last stage of a convolutional neural network (CNN) is a classifier. It is called a dense layer (fully connected layer), which is an artificial neural network (ANN) classifier. We needed to convert the output of the convolutional part of the CNN into a feature vector to be used by the ANN part of it, so we needed to add a flatten layer to connect the convolutional layer and the fully connected layer. It gets the output of the convolutional layers and flattens all its structure to create a single long feature vector to be used by the dense layer for the final classification [40]. Additionally, our network was further refined by adding dropout and regularization of the fully connected layer. Dropout is a technique for improving neural networks by reducing overfitting. [PERSON] et al. [43] pointed out that dropout of 50% of the hidden units and 20% of the input units significantly improved classification for a variety of different network architectures, but so far there is still no universal formula to set dropout rate. Dropout rate is related not only to the number of input variables but also to the number of the hidden units [44]. In this study, considering the input units are not large enough, we did not set the dropout rate in the input layer.