Sample interview questions: Can you discuss the different classification and segmentation techniques used in remote sensing data analysis?
Sample answer:
Classification Techniques
- Supervised Classification: Uses labeled training data to create a classifier that assigns pixels to predefined classes (e.g., land cover types). Examples include:
- Maximum Likelihood Classifier
- Support Vector Machines
- Random Forest
- Unsupervised Classification: Groups pixels based on their spectral or textural similarities without using training data. Examples include:
- K-Means Clustering
- ISODATA
- Fuzzy C-Means
Segmentation Techniques
Spatial Segmentation: Divides the image into regions based on spatial relationships between pixels. Examples include:
* Region Growing
* Watershed Segmentation
* Mean Shift
Spectral Segmentation: Groups pixels with similar spectral characteristics. Examples include:
* Normalized Cut
* Spectral Angle Mapper
* Minimum Spanning Tree
Object-Based Segmentation: Combines spatial and spectral information to identify meaningful objects (e.g., buildings, trees). Examples include:
* Mean Shift
* Graph-Based Segmentation
* Level Set Segmentation
Other Advanced Techniques
- Deep Learning and Convolut… Read full answer
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