Hi Rashmi,
Semantic segmentation of satellite aerial images involves classifying each pixel in the image into predefined categories or classes. Here's a step-by-step procedure to guide you through the process:
- Data Collection & Preprocessing: Obtain and label satellite images. Normalize and resize the data, and augment it if needed.
- Model Selection: Look into popular deep learning models for semantic segmentation like U-Net, SegNet, DeepLab, or FCN (Fully Convolutional Networks).Choose a model based on performance, complexity, and resource constraints.
- Setup Environment: Install deep learning libraries and prepare data loaders.
- Training: Define and compile the model with appropriate loss functions and optimizers. Train using training and validation datasets.
- Evaluation: Test the model on unseen data using metrics such as pixel accuracy and mean IoU.
- Post-processing: Refine predictions with morphological operations and optimize the model by tuning hyperparameters.
- Deployment: Deploy the trained model for segmenting new satellite images.
For more information on sematic segmentation you can refer below resources:
I hope it helps to achieve your semantic segmentation results!
