**5. Discussion**

The detection results of our method presented in the previous section have indicated that our end-to-end SR-detector network improved detection accuracy compared to several other methods. Our method outperformed the standalone state-of-the-art methods such as SSD or faster R-CNN when implemented in low-resolution remote sensing imagery. We used EESRGAN, EEGAN, and ESRGAN as the SR network with the detectors. We showed that our EESRGAN with the detectors performed better than the other methods and the edge-enhancement helped to improve the detection accuracy. The AP improvement was higher in high IoUs and not so much in the lower IoUs. We have also showed that the precision increased with higher resolution. The improvement of AP values for the OGST dataset was lower than that for the COWC dataset because the area covered by a tank was slightly bigger than that of a car, and tanks sizes and colors were less diverse than the cars.

Our experimental results indicated that AP values of the output could be improved slightly with the increase of training data. The results also demonstrated that we could use less training data for both the datasets to ge<sup>t</sup> a similar level of accuracy that we obtained from our total training data.

The faster R-CNN detector gave us the best result, but it took a longer time than an SSD detector. If we need detection results from a vast area, then SSD would be the right choice sacrificing some amount of accuracy.

We had large numbers of cars from different regions in the COWC dataset, and we obtained high AP values using different IoUs. On the other hand, the OGST dataset needed more data to ge<sup>t</sup> a general detection result because we used data from a specific area and for a specific season and this was one of the limitations of our experiment. Most likely, more data from different regions and seasons would make our method more robust for the use-case of oil and gas storage tank detection. Another limitation of our experiment was that we showed the performance of the datasets that contain only one class with less variation. We would be looking forward to exploring the performance of our method on a broader range of object types and landscapes from different satellite datasets.

We have used LR-HR image pairs to train our architecture, and the LR images were generated artificially from the HR counterparts. To our knowledge, there is no suitable public satellite dataset that contains both real HR and real LR image pairs and ground truth bounding boxes for detecting small objects. Therefore, we have created the LR images which do not precisely correspond to true LR images. However, improvement of resolution through deep learning always improved object detection performance on remote sensing images (for both artificial and real low-resolution images), as discussed in the introduction and related works section of this paper [5]. Impressive works [61,70] exist in literature to create realistic LR images from HR images. For future work, we are looking forward to exploring the works to create more accurate LR images for training.
