Types of Satellite Images

1. Multispectral

• Definition: Images captured at different wavelengths across the electromagnetic spectrum.
• Bands: Typically 3-10 bands, including visible and near-infrared.
• Uses: Agriculture (crop health), land cover classification, environmental monitoring.

2. Hyperspectral

• Definition: Captures a wide range of wavelengths, often hundreds of narrow bands.
• Bands: 100-200+ bands.
• Uses: Mineralogy, precision agriculture, detecting subtle changes in vegetation.

3. Radar

• Definition: Uses radio waves to capture images.
• Advantages: Can see through clouds and works in the dark.
• Uses: Topographic mapping, disaster monitoring (e.g., floods, earthquakes).

Deep Learning for Sentinel Raster Images and Satellite Images

Key Concepts

• Sentinel Satellites: Part of the European Space Agency's (ESA) Copernicus program. Provides high-resolution images for various applications.
• Raster Images: Digital images represented by a grid of pixels, each with a value representing information such as light intensity or spectral reflectance.

Applications of Deep Learning

1. Image Classification:

   • Objective: Categorize each pixel or region into different classes (e.g., water, forest, urban areas).
   • Techniques: Convolutional Neural Networks (CNNs) are commonly used.

2. Object Detection:

   • Objective: Identify and locate specific objects within an image (e.g., ships, buildings).
   • Techniques: Use of advanced CNNs like YOLO (You Only Look Once) and Faster R-CNN.

3. Change Detection:

   • Objective: Identify changes in a particular area over time using images from different dates.
   • Techniques: Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks can be used.

4. Image Segmentation:

   • Objective: Partition an image into segments that represent different objects or areas.
   • Techniques: U-Net and Fully Convolutional Networks (FCNs) are popular choices.

Challenges and Considerations

• Data Quality: Ensuring high-quality, cloud-free images.
• Data Volume: Handling large volumes of data requires robust storage and processing capabilities.
• Model Generalization: Ensuring models work well across different geographic regions and times.