Abstract
In this paper, we investigate and improve the use of a super-resolution approach to benefit the detection of small objects from aerial and satellite remote sensing images. The main idea is to focus the super-resolution on target objects within the training phase. Such a technique requires a reduced number of network layers depending on the desired scale factor and the reduced size of the target objects. The learning of our super-resolution network is performed using deep residual blocks integrated in a Wasserstein Generative adversarial network. Then, detection task is performed by exploiting two state-of-the-art detectors including Faster-RCNN and YOLOv3. Experiments were conducted on small vehicle detection from both aerial and satellite images from the VEDAI and xView data sets. Results showed that object-focused super-resolution improves the detection performance and facilitates the transfer learning from one data set to another.
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