# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 3 Result  
### Analysis of Building Detection Results Based on Shape Features  
The parameters to be set for building detection based on rectangular approximation are as follows: the detected line segment length screening range is set to [20; 400], the angle range of the two line segments considered to form a right-angle is [80\({}^{\circ}\), 100\({}^{\circ}\)], and the maximum distance between endpoints is 15. The experimental results are shown in Figure 6.  
Figure 6: Building detection results based on rectangular approximation. (**a**) Image I mask map, (**b**) Image I detection result, (**c**) Image II mask map, (**d**) Image II detection result, (**e**) Image III mask map, (**f**) Image III detection result, (**g**) Image IV mask map, (**h**) Image IV detection result.  
The saliency maps obtained by combining the bounding box proposal are shown in Figure 12. To observe the improvement of the saliency map more visually, the change of the histogram before and after obtaining the foreground mask map is shown in Figure 13. It can be seen that the interference in the environmental background except for the building  
Figure 11: Foreground mask maps. (**a**) Image I, (**b**) Image II, (**c**) Image III, (**d**) Image IV.  
Figure 9: The improved boundary priori saliency maps. (**a**) Image I, (**b**) Image II, (**c**) Image III, (**d**) Image IV.  
The experimental parameters of this paper are designed as follows:  
1. The number of super-pixel nodes \(N\) used in the SLIC model: The SLIC model abstracts the input image into uniform and compact regions. If \(N\) is too small, different objects will be mapped to the same super-pixel, which will lead to a decrease in the accuracy of saliency object detection. If \(N\) is too large, saliency objects will be mapped to different super-pixels, which may incorrectly suppress saliency regions. The parameter \(N=100\) is set in the experiment based on compactness and local contrast saliency detection method. The improved saliency detection method with manifold ranking and boundary prior sets the parameter \(N=W\cdot H/600\), where \(W\) and \(H\) are the width and height of the experimental image;
2. \(\sigma\) for controlling the decay rate of the exponential function: The highest accuracy was achieved when \(\sigma^{2}=0.1\) in the experiment;
3. The equilibrium parameters of the manifold ranking algorithm: The parameters of the literature \"Ranking on Data Manifolds\" [17] are set with \(\alpha=0.99\) and \(\mu_{2}=0.5\);
4. After experimental verification, the fusion parameters are chosen as follows: \(\varepsilon_{com}=0.3\), \(\varepsilon_{loc}=0.2\), \(\varepsilon_{obj}=0.5\) for the best detection of the saliency map.  
After obtaining the saliency map, the threshold value \(T_{f}\in[0,255]\) of the saliency map is changed from 0 to 255 in order to obtain a better detection effect, and the value of the evaluation index corresponding to the threshold value is calculated by comparing the binary mask map and the binary ground truth map obtained by setting different thresholds of the saliency map, and the change curve of the evaluation indicator is shown in Figure 14.  
Figure 9 shows the curves of accuracy, precision, recall and F1 score of the mask map calculated from the saliency map compared with the ground truth map for the case of the threshold value \(T_{f}\in[0,255]\). The blue line marked in the figure is the threshold position with the highest F1 score, and the adaptive threshold \(T_{a}\) is defined as Equation (28).  
\[T_{a}=\frac{2}{W\cdot H}{\sum_{i=1}^{W}{\sum_{i=1}^{H}{S(i,j)-10}}}, \tag{28}\]  
Figure 14: Variation of each indicator for threshold \(T_{f}\in[0,255]\). **(a)** Image I, **(b)** Image II, **(c)** Image III, **(d)** Image IV.  
where \(W\) and \(H\) are the width and height of the saliency map \(S\). The saliency map is thresholded with \(T_{a}\) to obtain a mask map for building target detection, which is used for the next step of image fusion.