Toward object alphabet augmentation for object detection in very high‐resolution satellite images. (20th December 2021)

• Record Type: Journal Article
• Authors: Laban, Noureldin; Abdellatif, Bassam; Ebeid, Hala M.; Shedeed, Howida A.; Tolba, Mohamed F.
• Is Part Of: Concurrency and computation. Volume 34:Number 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-20
• Subjects: convolutional neural network -- deep learning -- object augmentation -- object detection -- remote sensing -- satellite images; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6785
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Object detection in very high‐resolution satellite images has become an important tool in many fields. So there is a vital need to build a more precise and accurate detector. There is a great challenge to detect very small and condensed objects with their different semantics shapes. Recently, convolutional neural networks with different structures have achieved awesome performance. We propose object alphabet augmentation method that extracts object instants during the training process to form an alphabet for these objects in a newly generated dataset. The proposed method uses the DarkNet‐53 framework to get the main features for each object using two new generated datasets for alphabet augmentation up‐down where objects are placed in up down, and alphabet augmentation horizontal where objects are placed in their original setting. Experiments are conducted on datasets randomly generated from the DOTA dataset. The experimental results show that the proposed method has improved the accuracy of detection of the target objects of the DOTA dataset for most object classes, especially for the up‐down dataset.