# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 4 Discussion  
From the experimental results, it can be seen that typical straight line segment detection methods include Hough line detection, LSD and radon transform. Among them, the detection results of the better performing LSD have broken line segments, which is unfavorable to the extraction of right-angle primitives of buildings. Therefore, the constructed broken straight line segment criterion is used to effectively recover the line segments in the image by connecting the broken line segments with parallelism, covariance and broken distance criterion. The detection method of right-angle primitives has ambiguity to tolerate the case of non-adjacent perpendicular straight line segments, and we establish a voting space \(V\in R^{M<N>A}\) of position angles for the extraction of potential right-angle primitives, which obtains the building area and constrains the contour boundary of the building.  
The compactness-based, local contrast-based, and boundary prior-based saliency maps are fused, and the weights of the three saliency maps are assigned to obtain the saliency map fusion result. For overcoming the complex environment around the building, a bounding box proposal is introduced to optimize the saliency map detection results. Finally, the detection method based on rectangular approximation and the saliency detection method are combined to further improve the detection of buildings.  
The proposed method preserves the contour boundary of the building well, and is an effective method for building detection. Among the existing building target detection methods, deep learning-based building detection methods are more widely used, and we find that many studies are based on pixels to classify buildings. The pixel-based  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Rectangular & 0.9080 & 0.6970 & 0.7289 & 0.7126 \\ approximation-based & 0.9211 & 0.9038 & 0.5550 & 0.6877 \\ Fusion & 0.9166 & 0.6965 & 0.8275 & 0.7564 \\ Interference removal & 0.9257 & 0.7430 & 0.8018 & 0.7712 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Image IV detection results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Rectangular & 0.8689 & 0.6429 & 0.6547 & 0.6487 \\ approximation-based & 0.9134 & 0.8317 & 0.6663 & 0.7398 \\ Saliency-based & 0.8842 & 0.6445 & 0.8336 & 0.7270 \\ Interference removal & 0.8958 & 0.6807 & 0.8216 & 0.7445 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Image II detection results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Rectangular & 0.8689 & 0.6429 & 0.6547 & 0.6487 \\ approximation-based & 0.9134 & 0.8317 & 0.6663 & 0.7398 \\ Fusion & 0.8842 & 0.6445 & 0.8336 & 0.7270 \\ Interference removal & 0.8958 & 0.6807 & 0.8216 & 0.7445 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Image III detection results.  
classification and recognition methods can hardly consider the neighborhood information around the pixels, so the contours of buildings cannot be well guaranteed [23].  
The existing wavelet transform-based building detection methods, which use different data sources for research, use the binary wavelet transform to detect building edges, which is difficult for the detection of complex building backgrounds and the study of complex building roofs [24; 25; 26]. Therefore, enhancing building detection constraints can be effective for building extraction, and ensuring the geometric contour of the building while guaranteeing the accuracy of building extraction is the next focus of our research.