Abstract
Object detection in remote sensing (RS) images is a challenging task due to the difficulties of small size, varied appearance, and complex background. Although a lot of methods have been developed to address this problem, many of them cannot fully exploit multilevel context information or handle cluttered background in RS images either. To this end, in this paper, we propose a feature fusion and filtration network (F3-Net) to improve object detection in RS images, which has higher capacity of combining the context information at multiple scales while suppressing the interference from the background. Specifically, F3-Net leverages a feature adaptation block with a residual structure to adjust the backbone network in an end-to-end manner, better considering the characteristics of RS images. Afterward, the network learns the context information of the object at multiple scales by hierarchically fusing the feature maps from different layers. In order to suppress the interference from cluttered background, the fused feature is then projected into a low-dimensional subspace by an additional feature filtration module. As a result, more relevant and accurate context information is extracted for further detection. Extensive experiments on DOTA, NWPU VHR-10, and UCAS AOD datasets demonstrate that the proposed detector achieves very promising detection performance.
Highlights
With the fast development of airborne and spaceborne sensors, remote sensing (RS) images have become widely available, offering new opportunities to observe and interpret the Earth
HA and swimming pool (SP) categories often appear in complex scenes, which interferes the detector by introducing a large number of false positives
A F 3-Net is introduced for object detection in RS images, which captures the context information by feature fusion and feature filtration modules
Summary
With the fast development of airborne and spaceborne sensors, remote sensing (RS) images have become widely available, offering new opportunities to observe and interpret the Earth. Object detection aims at simultaneously determining the location and category of objects of interest in the RS image. It is an important task in practical applications of RS images such as resource acquisition, disaster monitoring, urban planning, etc. Object detection in RS images can be grouped by template matching-based, knowledge-based, and machine learning-based methods [7]. Treating this task as a classification problem, machine learning-based methods stand out due to the advance of powerful feature representations and classifiers. Increasingly more attention has been focused on deep detectors with high-level learned features, driven by strong capability of deep learning models as feature extractors and easy access to large-scale RS datasets such as DOTA [2], NWPU VHR-10 [10], UCAS-AOD [11], etc
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