An active region corrected method for weakly supervised aircraft detection in remote sensing images

Aircraft detection is a challenging task in remote sensing images which attract increasing attention in recent years. Existing methods based on fully-supervised convolutional neural networks (CNN) require expensive labeling information such as bounding box, which is time consuming and difficult to obtain. Recently, weakly supervised methods only using image-level labels has drawn increasing attention in natural imagery. An approach called class activation map (CAM) based on weakly supervised performs well in natural scene images for object detection, but there is a problem when using it in remote sensing images: inaccurate localization. In this paper, we propose a method called Active Region Corrected (ARC) to locate aircraft accurately. We find that generating the localization map in the classified network by extracting the feature before the last pooling layer contains more accurate position information but a lot of noise, and then we use the CAM to generate a localization map which contains rough location information of aircraft. Combining these two localization maps we can get the exact position of the aircraft. Experiments conducted on data set verify that our proposal obtains a superior performance on aircraft detect and localization in remote sensing images.