Artificial Neural Networks (ANNs) used for change detection in remotely sensed images


  • Annu Sharma, Praveena Chaturvedi, Sakshi Kathuria, Amit Verma, Elangovan Muniyandy, Mohd Naved


Change detection, remote sensing, ANNs, urban areas, applications,


This paper examines the application of semi-supervised Artificial Neural Networks (ANNs) in the change detection of remotely sensed images. Relying on the analysis of multi –temporal satellite images to detect altercations caused by natural or human activities is crucial for change detection for monitoring environmental changes and urban expansion. Recent advancements in Artificial Intelligence (AI) particularly semi-supervised ANNs, have significantly improved the accuracy and efficiency of change detection processes. This review highlights various methodologies and techniques employed in the field, including the integration of Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) for enhanced feature extraction and classification. The paper discusses the application of these methods across different scenarios such as agricultural yield prediction, urban growth monitoring and environmental surveillance underlining the importance of ANNs in advancing remote sensing capabilities.


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How to Cite

Annu Sharma, Praveena Chaturvedi, Sakshi Kathuria, Amit Verma, Elangovan Muniyandy, Mohd Naved. (2024). Artificial Neural Networks (ANNs) used for change detection in remotely sensed images. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 538–547. Retrieved from



Research Article