Fusion based feature reinforcement component for remote sensing image object detection

Abstract

In recent years, convolutional neural networks (CNN) have been extensively used for generic object detection due to their powerful feature extraction capabilities. This has hence motivated researchers to adopt this technology in the field of remote sensing. However, remote sensing images can contain large amounts of noise, have complex backgrounds, include small dense objects as well as being susceptible to weather and light intensity variations. Moreover, from different shooting angles, objects can either have different shapes or be obscured by structures such as buildings and trees. Due to these, effective features extraction for proper representation is still very challenging from remote sensing images. This paper therefore proposes a novel remote sensing image object detection approach applying a fusion-based feature reinforcement component (FB-FRC) to improve the discrimination between object feature. Specifically, two fusion strategies are proposed: (i) a hard fusion strategy through artificially-set rules, and (ii) a soft fusion strategy by learning the fusion parameters. Experiments carried out on four widely used remote sensing datasets (NWPU VHR-10, VisDrone2018, DOTA and RSOD) have shown promising results where the proposed approach manages to outperform several state-of-the-art methods.