## 2 Related Works  
### Computer Vision Technology  
Since the middle of the 20 th century, image classification technology in the field of computer vision has made constant progress. With the rise of deep learning in recent years, image classification based on deep convolutional neural network (DCNN) has achieved positive results in intelligent data acquisition and efficient processing. At present, image classification technology has many applications in aerial remote sensing [11], ocean remote sensing [12], face recognition [13], and so on. Convolution operation is a multi-layer feedforward neural network model. Its network structure is characterized by the use of a separate set of convolution kernels in each layer, which helps to extract useful features from locally relevant data points. In the training process, CNN learns by BP (back propagation) algorithm and updates the weights of network nodes. The continued success of BP algorithms and CNN has led to network models such as LeNet [14], AlexNet [15], VGG [16], GoogLeNet [17], ResNet [18], and MobileNet [19]. At the same time, because CNN adopts the network structure with core weight sharing, it can gain a better learning effect by increasing network depth when dealing with complex problems. The basic architecture of CNN is mainly composed of a convolution layer, activation layer, normalization layer, pooling layer, and full connection layer. The function of the convolution layer is to extract the features of input data. Convolution is usually calculated with the size of 3 \(\times\) 3, 5 \(\times\) 5, or 7 \(\times\) 7 convolution kernels to obtain a multidimensional feature map. The common convolution operations are standard convolutions, transpose convolution [20], dilated convolution [21], depth separable convolution [22], deformable convolution [23], and so on. Standard convolution is a process in which the convolution kernel slides onto the image and calculates the gray values of all image pixels through a series of matrix operations. This process is also known as the down-sampling process. Transposed convolution realizes the reverse operation of convolution, also known as up-sampling, and is widely used in semantic segmentation. Compared with the conventional convolution, dilated convolution increases the distance between the values of the convolution kernel, so as to extract better features. Deep separable convolution realizes the separation of channels and regions in normal convolution, which can greatly reduce the parameters of a network model. It is applied to the lightweight network model MobileNet. Deformable convolution adds an additional direction vector to each element of the convolution kernel, which can automatically adjust its shape according to the different scale or deformation of the object to better extract the input features. The function of the activation layer is to increase the nonlinearity of the neural network model through an activation function. Common activation functions are the rectified linear unit (ReLU) [24], sigmoid function [25], and tan hyperbolic (tanh) functions. ReLU is the most significant unsaturated activation function, which is more efficient than sigmoid and tanh. Their expressions are shown in Equations (1)-(3). \[Relu(x)=\max(0,x) \tag{1}\]  
\[\sigma(x)=\text{sigmoid}(x)=1/\left(1+e^{-x}\right) \tag{2}\]  
\[\text{Tan}(x)=(e^{x}-e^{-x})/\left(e^{x}+e^{-x}\right) \tag{3}\]  
The function of batch normalization (BN) [26] enables researchers to select larger learning rates. While the training speed of the model grows rapidly, the model has fast convergence, and the problems of gradient dispersion and gradient explosion are avoided. Its expression is shown in Equation (4).