Hybrid AI Techniques for Remote Sensing and Environmental Change Detection
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This chapter explores the application of hybrid Artificial Intelligence (AI) techniques for remote sensing and environmental change detection. A novel methodology is presented that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal analysis, and attention mechanisms for feature fusion. The proposed hybrid model is designed to accurately identify and classify changes in land cover using multi-temporal satellite imagery. The performance of the model is evaluated on a simulated Sentinel-2 dataset, demonstrating its superiority over traditional approaches and individual deep learning methods. This chapter provides a comprehensive overview of the methodology, experimental results, and a discussion of the implications for environmental monitoring and management. The findings indicate that hybrid AI approaches can significantly enhance the accuracy and reliability of change detection in complex and dynamic environments.
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Principles of Hybrid Intelligent Systems
