Deep Learning Architectures for Remote Sensing

Deep learning has revolutionized remote sensing image analysis, enabling unprecedented accuracy in earth observation tasks. This chapter comprehensively examines four fundamental deep learning architectures—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers—and their applications in remote sensing for earth sciences. CNNs excel at spatial feature extraction for land cover classification and object detection, achieving accuracies exceeding 95% in various applications. RNN and LSTM architectures effectively model temporal dependencies in satellite image time series, demonstrating superior performance in crop monitoring and vegetation dynamics prediction. Transformer-based models, representing the latest paradigm shift, leverage self-attention mechanisms to capture global contextual relationships, outperforming traditional CNNs in scene classification and semantic segmentation tasks.

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