Object Detection and Recognition in Remote Sensing Images by Employing a Hybrid Generative Adversarial Networks and Convolutional Neural Networks

• Published in: International journal of advanced computer science & applications 2023, Vol.14 (9)
• Main Authors: Deshmukh, Araddhana Arvind, Kumari, Mamta, Krishnaiah, V.V. Jaya Rama, Bandhekar, Shweta, Dharani, R.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Datasets; Feature extraction; Generative adversarial networks; Machine learning; Model accuracy; Object recognition; Remote sensing; Synthetic data
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2023.0140965

Due to diverse backdrops, scale fluctuations, and a lack of annotated training data, the identification and recognition of objects in remote sensing images present major problems. In order to overcome these difficulties, this work suggests a novel hybrid technique that blends GAN and CNN. The suggested approach expands the small labelled dataset by synthesising realistic training examples using the generative abilities of GANs. The samples generated capture the various variances and backgrounds found in remote sensing photos, improving the object identification and recognition model's capacity to generalise. Additionally, CNNs, which are recognised for their outstanding feature extraction skills, are incorporated into the hybrid approach, enabling precise and reliable object identification and recognition. The model's CNN component is developed using both real and synthetic data, effectively combining the advantages of both fields. Several experiments are conducted on a large dataset of satellite photos to evaluate the performance of the proposed method. The results demonstrate that the hybrid model, with accuracy 97.32%, outperforms traditional approaches and pure CNN-based approaches in terms of dependability and resilience. The model may be efficiently generalised to unknown remote sensing images thanks to the GAN-generated samples, which bridge the gap among synthetic and actual data. The hybrid methodology used in this study demonstrates the possibility of merging GANs and CNNs for item detection and recognition using deep learning in remote sensing images.