Watermarking-Enabled VGG19 for Accurate Land Cover Classification in Satellite Imagery

Author(s)	Mr. Samender Singh, Dr. Mukesh ingla
Country	India
Abstract	Analysing land use and land cover images (LULC) plays a role in applications such as monitoring, managing natural resources and responding to disasters. This research investigates the effectiveness of using the VGG19 model and the Adam optimizer to classify satellite images into four habitat categories: cloudy, desert, green area, and water. By working with a dataset of 5631 photos from sensors and Google Maps snapshots transfer learning is employed to tailor the trained VGG19 model for this specific classification task. Through experimentation involving preparation, model training and evaluation an impressive accuracy score of 98.2% was achieved. Visual aids like graphs showing accuracy and loss trends, a confusion matrix, and performance parameter calculations were used to highlight the robustness and reliability of the model. This research contributes to enhancing automated processing of satellite images providing insights, for monitoring, urban planning, and disaster response efforts. Future studies could focus on improving classification accuracy and efficiency in satellite image analysis by exploring the scalability, adaptability, and integration of deep learning architectures.
Keywords	Land use and land cover, satellite image classification, VGG19, transfer learning
Field	Computer Applications
Published In	Volume 7, Issue 1, January-February 2026
Published On	2026-02-11

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