Authors:
Yassin Terraf
1
;
El Mercha
1
and
Mohammed Erradi
2
Affiliations:
1
HENCEFORTH, Rabat, Morocco
;
2
ENSIAS, Mohammed V University, Rabat, Morocco
Keyword(s):
Object Detection, Deep Learning, Attention, Remote Sensing Images.
Abstract:
Object detection in remote sensing images has been widely studied due to the valuable insights it provides for different fields. Detecting objects in remote sensing images is a very challenging task due to the diverse range of sizes, orientations, and appearances of objects within the images. Many approaches have been developed to address these challenges, primarily focusing on capturing semantic information while missing out on contextual details that can bring more insights to the analysis. In this work, we propose a Non-Local Context-Aware Attention (NLCAA) approach for object detection in remote sensing images. NLCAA includes semantic and contextual attention modules to capture both semantic and contextual information. Extensive experiments were conducted on two publicly available datasets, namely NWPU VHR and DIOR, to evaluate the performance of the proposed approach. The experimental results demonstrate the effectiveness of the NLCAA approach against various state-of-the-art me
thods.
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