## 3 Method
### Radiance Correction Based on the Incident Angle  
Strictly, the radiance should be calculated using the FOV. However, the FOV needs the corresponding ground coordinates. Thus, we used the simpler incident angle to simulate the effect of the FOV. Given that the main optical axis direction is \(\left(f_{x},f_{y},f_{z}\right)\) and the incidence direction is \(\left(\lambda_{x},\lambda_{y},\lambda_{z}\right)\), the angle between the incidence ray and the main optical axis is as follows:  
\[A_{f}^{\lambda}=\arccos\frac{\left(f_{x},f_{y},f_{z}\right)\left(\lambda_{x},\lambda_{y}\lambda_{z}\right)}{\left|f_{x},f_{y},f_{z}\right|\left|\lambda_{ x},\lambda_{y}\lambda_{z}\right|}. \tag{8}\]  
We assumed that if the ray \(\mathbf{r}\) is imported from the direction of the main optical axis into the optical center, the radiance produced is \(R_{\mathbf{r}}\). Due to the incidence angle, the actual incident radiance \(\overline{R_{r}}\) should be less than \(R_{\mathbf{r}}\), i.e., the z-direction component:  
\[\overline{R_{\mathbf{r}}}=\left.R_{\mathbf{r}}\right|_{z}=R_{\mathbf{r}} \cos A_{f}^{\lambda}. \tag{9}\]  
We first applied position encoding to the incident angle to utilize the incident information, which facilitated the subsequent network capturing of the details:  
\[\varphi(\lambda)=(\sin(\eta_{1}\lambda),\cos(\eta_{1}\lambda),\ldots,\sin( \eta_{k}\lambda),\cos(\eta_{k}\lambda)), \tag{10}\]  
where the frequencies \(\eta_{1},\eta_{2},\ldots,\eta_{k}\) are learnable parameters during training; \(k\) is the positional encoding dimension; and \(\varphi(\lambda)\) is passed into an exclusive MLP with parameters \(W_{\lambda}\) and \(b_{\lambda}\) to obtain a refined representation:  
\[\overline{\varphi(\lambda)}=\varphi(\lambda)\times W_{\lambda}+b_{\lambda}. \tag{11}\]  
Then, we concatenated \(\varphi(\lambda)\) with the position coordinates \(\mathbf{x}\) into the model \(\mathcal{F}\) to help decode the radiance:  
\[f_{\lambda+}=\text{Concat}([\mathbf{x},\overline{\varphi(\lambda)}]). \tag{12}\]  
Since the actual ray radiance obtained using a single pixel is smaller than it should be due to \(\lambda\), we re-deduced the color rendering process according to Equation (9):\[\mathbf{c}(\mathbf{r})=\sum_{i=1}^{N}T_{i}\alpha_{i}\mathbf{c}_{i}\mathbf{c}\text{ cos}A_{f}^{\lambda}. \tag{13}\]  
Then, for each ray \(\mathbf{r}(t)=\mathbf{o}+t\mathbf{d}\), the interval can also be expressed by \(t\), i.e., \([t_{\text{min}},t_{\text{max}}]\), for the definite starting and ending points \(t_{\text{min}}=0\) and \(t_{\text{max}}=\left\|\mathbf{x_{\text{end}}}-\mathbf{x_{\text{start}}}\right\| _{2}\). Since working with UTM coordinates is impractical because of the large values, we normalized all the points to the interval \([-1,1]\), which was obtained according to the navigation height data recorded by the UAV. According to Equation (13), a point can be considered on the surface when the transmittance \(T_{i}\) of the \(i\)-th point in the ray is estimated as being 0. The depth can be estimated as follows:  
\[d(\mathbf{r})=\sum_{i=1}^{N}T_{i}\alpha_{i}t_{i}. \tag{14}\]  
After inverse normalization and coordinate transformation, the obtained depth can be restored to geographical coordinates under the UTM system, and the digital surface model can be directly restored via the same processing for each ray.