how to do semantic segmentation for satellite aerial image?

I am working on semantic segmentation of a single satellite aerial image. I am new to this segmentation how to segment aerial images into different classes. I needed a step by step procedure for semantic segmentation

Answer 1

[prabhat kumar sharma]

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:

1. Data Collection & Preprocessing: Obtain and label satellite images. Normalize and resize the data, and augment it if needed.
2. 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.
3. Setup Environment: Install deep learning libraries and prepare data loaders.
4. Training: Define and compile the model with appropriate loss functions and optimizers. Train using training and validation datasets.
5. Evaluation: Test the model on unseen data using metrics such as pixel accuracy and mean IoU.
6. Post-processing: Refine predictions with morphological operations and optimize the model by tuning hyperparameters.
7. Deployment: Deploy the trained model for segmenting new satellite images.

For more information on sematic segmentation you can refer below resources:

https://www.mathworks.com/help/vision/ug/getting-started-with-semantic-segmentation-using-deep-learning.html

https://www.mathworks.com/help/vision/ug/object-detection-in-large-satellite-imagery-using-deep-learning.html

https://www.mathworks.com/matlabcentral/fileexchange/110745-satellite-image-semantic-segmentation?s_tid=prof_contriblnk

I hope it helps to achieve your semantic segmentation results!