# Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
## 5 Deep Feature Extraction with Context Scene Feature Fusion and Detection
### Results and Analysis
#### 5.5.3 Result Analysis
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-3}  & **SMALL-FIELD-RSIs** & **LARGE-FIELD-RSIs** \\ \hline objNet-RSI & 93.51\% & 94.24\% \\ objNet-TL1 & 96.63\% & 95.52\% \\ objNet-TL2 & 97.23\% & 96.86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The accuracy of objNet based on different transfer learning methods. Figure 18: Visualization of the first-level convolutions in different network models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **AlexNet-RSI** & **AlexNet-TL** & **CaffeNet-TL** & **Places205 -AlexNet-TL** & **Hybrid -AlexNet-TL** \\ \hline accuracy & 89.76\% & 94.04\% & 93.87\% & 92.68\% & **94.81\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification accuracy of the network transferred from different models. Figures 19 and 20 show the comparison of the results of the four experiments on two different dataset sizes. From the results of the experiments objNet and objNet-TL, it was revealed that the transfer learning of object the feature extraction network could improve the whole detection performance significantly. Before transfer learning, the accuracy of the curve decreased rapidly with the recall increasing, and the curve decreased slowly after transfer learning, which shows that the extracted feature of network after transfer learning made the classifier discriminate better, which means the transfer learning was effective. It can be concluded from the experiment objNet-TL-seeNet-TL that the detection efficiency on the two remote sensing datasets was better than that for the experiment without context scene feature fusion, indicating the effectiveness of the scene feature fusion. However, compared with objNet-TL-seeNet and objNet-TL, it is shown that when transfer learning was not employed, the improvement was not obvious, the possible reason for which being that the seeNet network has too many parameters for the limited remote sensing scene data, and if we directly trained the network using limited data without transfer learning, it would easily to over-fit, so that the extracted features would not be representative. Finally, we compared the proposed algorithm objNet-TL-seeNet-TL with the other algorithms. Firstly, in order to prove the effectiveness of the deep features, we used the HOG algorithm [34] to extract the feature of the candidate region and used the SVM algorithm and the hard negative mining method to train the detector; we called it HOG-SVM. In addition, this paper compared the R-CNN algorithm [14]. This algorithm achieved a breakthrough in the PASCAL VOC2007 object detection task. It first uses the selective search algorithm to generate about 2000 candidate regions for each image and then uses the AlexNet network to extract features, and finally classifies each region using linear SVM classification. From the analysis of Section 4.3.2, we can see that the selective search algorithm \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Scene Feature Fusion** & **seeNet Transfer Learning** & **objNet Transfer Learning** \\ \hline objNet & \(\times\) & – & \(\times\) \\ objNet-TL & \(\times\) & – & \(\surd\) \\ objNet-TL-seeNet & \(\surd\) & \(\times\) & \(\surd\) \\ objNet-TL-seeNet-TL (ours) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: List pf the configurations of each experiment. Figure 19: The performance comparison of four experiments on the SMALL-FIELD-RSIs dataset. Figures 23 and 24 show the results of the objNet-TL-seeNet-TL algorithm on the SMALL-FIELD-RSIs and LARGE-FIELD-RSIs datasets, respectively. In each image, the red rectangles indicate the real objects marked by the dataset and the blue rectangles the final detection result of our algorithm. It appears that the vast majority of airplane objects can be correctly detected.