Advanced CNN Architectures: Deep CNN Architectures

VGG (Visual Geometry Group)

• Architecture: Several layers (e.g., VGG16, VGG19) with small convolution filters (3x3).
• Features: Deep networks with repeated layers, increasing depth.
• Use Case: Effective for feature extraction in images, not ideal for real-time applications due to depth.

ResNet (Residual Network)

• Architecture: Deep networks using residual blocks with skip connections.
• Features: Mitigates vanishing gradient problem, allows training of very deep networks (e.g., ResNet50, ResNet101).
• Use Case: Excellent for deep image classification tasks, including satellite images.

Inception Networks (GoogLeNet)

• Architecture: Utilizes inception modules with parallel convolutional operations of different sizes.
• Features: Efficient use of computation resources, improves learning capability by capturing features at different scales.
• Use Case: Suitable for image classification tasks where capturing diverse features is crucial, applicable to satellite and Sentinel images.

Applications in Sentinel and Satellite Images

• Feature Extraction: CNNs extract hierarchical features (textures, shapes) from images, vital for classification and segmentation.
• Classification: Architectures like ResNet excel in classifying complex satellite/Sentinel images into predefined categories.
• Semantic Segmentation: Utilized in dividing images into regions for detailed analysis, aiding tasks like land cover mapping.

Considerations

• Computational Efficiency: Balance between model complexity (e.g., ResNet) and computational cost for satellite/Sentinel image analysis.
• Transfer Learning: Pre-trained models (e.g., on ImageNet) can be fine-tuned for specific satellite/Sentinel datasets, reducing training time and enhancing accuracy.