Enhanced Super-Resolution via Squeeze-and-Residual-Excitation in Aerial Imagery

Abstract

Super-resolution (SR) presents us with an outstanding technique of enhancing applications associated with aerial and remote-sensing imagery, hence, tasks like classification, segmentation and object detection can benefit significantly from well-performing SR models. Extensive research is being done in the field of SR for both ground-level and aerial imagery where convolutional neural networks (CNN) have attained incredible progress. Numerous deep CNNs use the attention mechanism in their architectures and one such mechanism is the Squeeze-and-Excitation (SE) inter-channel attention. Although SE block has enhanced the performance of many models, there is no residual mechanism used within its structure. Therefore, in this paper, we propose the Squeeze-and-Residual-Excitation (SRE) attention block. SRE improves upon the SE block by using residual mechanism within its structure to deliver performance gain in the task of SR. Based on our SRE attention mechanism we propose an enhanced SR framework for remote-sensing imagery. We call our model the Squeeze-and-Residual-Excitation Holistic Attention Network (SRE-HAN) that outperforms other attention-based deep SR models for two levels of resolution enhancement: 4x- and 8x-upsampling on two diverse aerial imagery datasets: Satellite Imagery Multi-Vehicles Dataset (SIMD) consisting of 5000 high-resolution (HR) aerial images, and Cars-Overhead-With-Context (COWC). Furthermore, by using YoloV5 object-detection model, we carry out multiple experiments to substantiate the effectiveness of these SR models on the task of object detection on SIMD.