Introduction to object detection

• Object Detection Basics:

  • Definition: Identifying and locating objects of interest within satellite images.
  • Applications: Urban planning, disaster response, agriculture monitoring.

• Challenges:

  • Resolution: Satellite images often have lower resolution compared to traditional photos.
  • Variability: Images can vary in lighting, weather conditions, and angles.

• Techniques Used:

  • Convolutional Neural Networks (CNNs): Primary tool for feature extraction and detection.
  • Region-based CNNs: Divide image into regions, classify each for potential objects.
  • Anchor Boxes: Predicts regions likely to contain objects based on predefined sizes and shapes.

• Datasets:

  • Annotated Datasets: Labeled images used to train models (e.g., COCO dataset).
  • Domain-Specific Datasets: Tailored datasets for specific satellite image applications.

• Evaluation Metrics:

  • Intersection over Union (IoU): Measures overlap between predicted and actual object regions.
  • Mean Average Precision (mAP): Average precision across all object categories.

• Applications in Sentinel Data:

  • Vegetation Monitoring: Detecting changes in agricultural areas.
  • Urban Development: Tracking construction and infrastructure changes.
  • Disaster Response: Identifying affected areas and damaged infrastructure.

• Future Trends:

  • Integration with AI: Combining object detection with AI for real-time analysis.
  • Improving Resolution: Advances in satellite technology improving image clarity.
  • Automated Analysis: Moving towards fully autonomous satellite image analysis systems.