# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 4 Size-Scalable Candidate Region Proposal Algorithm
### Experiments
#### 4.3.2 Results and Analysis  
The experiments in this section were divided into two parts: (1) we briefly analyzed the applicability of the selective search algorithm for candidate region proposal in remote sensing images; (2) we evaluated the result of the BING algorithm and pBING algorithm. In the experiment, each dataset was divided into a training set and a test set, where the test set was 20%. The DR and MABO below were the average of all the test image detection results. Table 2 shows the average number of candidate windows and running times of the selective search algorithm on different sizes of remote sensing images. This experiment was performed in fast mode using only two color spaces and two similarity functions. As in the remote sensing image, when the spatial resolution of the image is determined, the size range of the object in the image can be determined. Using this prior knowledge, we filtered out the irrational size of the candidate windows, and the filtered results are shown in the third row of Table 2. It can be found that the average number of candidate windows generated by the algorithm increased significantly, and the running time of the algorithm increased sharply with the increase of the image size. In addition, in the experimental process, we found that when the image size increased to 2000 \(\times\) 2000 pixels, the machine was out of memory, and the MATLAB compiler was in a stuck situation. Therefore, this algorithm has a poor scalability for image size. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Name** & **Spatial Resolution** & **Image Size (Pixels)** & **Number of Images** & **Number of Objects** \\ \hline SMALL-FIELD-RSIs & 0.6 m & 1300 \(\times\) 800 & 980 & 7452 \\ LARGE-FIELD-RSIs & 0.6 m & 5000 \(\times\) 5000 & 109 & 3380 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The experimental dataset. Figure 4: The high-resolution remote sensing image of Baoan Airport. In summary, the selective search algorithm had higher complexity, and the number of candidate windows was larger, which was not scalable for the image size. The following are the performances of BING algorithm and pBING algorithm when extracting candidate regions in two high-resolution remote sensing images. As shown in Figure 5, when the number of candidate windows was 1000, the DR of the pBING algorithm was 97.21% on the SMALL-FIELD-RSIs, which was higher than the original algorithm (95.74%). The MABO score increased from 63.43-65.30%, which indicates that the improved algorithm had a better quality of the candidate region proposal on the remote sensing images. In addition, it shows that when the number of candidate windows was 2000, the detection rate of pBING algorithm in this dataset was as high as 98.9%. Therefore, as the input of the second stage, the number of candidate windows in this stage can be 2000, that is, for each image, we output the first 2000 candidate windows of the pBING detection results to further classify. Figure 6 shows the performance of the two algorithms on dataset LARGE-FIELD-RSIs. Since the image of the dataset is relatively large, we mark DR and MABO of the algorithm when the given number of candidate windows is 8000. Figure 7 shows the details. It appears that DR and MABO are increasing as the number of candidate regions increases. Obviously, that is because that a remote sensing image with large size usually has more objects, and search space of objects' possible locations significantly increases, and then more candidate windows are needed in order to achieve a certain detection rate. As shown in Figure 7, in order to ensure that the follow-up classification to achieve a certain rate of recall, in this stage we select the first 9000 candidates as the input of the second stage. However, since the image size is not fixed in this dataset: the average size of images varies from 2000 pixels to 9000 pixels. In order to further reduce the number of candidate windows, we select the number of output candidate according to the image size.