Useful Links
Physics
Applied and Interdisciplinary Physics
Remote Sensing
1. Fundamentals of Remote Sensing
2. Physical Principles of Remote Sensing
3. Remote Sensing Platforms
4. Remote Sensing Sensors
5. Remote Sensing Data Characteristics
6. Data Formats and Standards
7. Digital Image Processing Fundamentals
8. Image Pre-processing
9. Image Enhancement Techniques
10. Image Transformations
11. Image Classification and Analysis
12. Change Detection Analysis
13. Advanced Remote Sensing Systems
14. Remote Sensing Applications
Image Classification and Analysis
Visual Image Interpretation
Elements of Image Interpretation
Tone and Color
Texture
Shape
Size
Pattern
Shadow
Association
Site and Situation
Interpretation Keys
Stereoscopic Interpretation
Multi-temporal Analysis
Digital Classification Methods
Unsupervised Classification
K-Means Clustering
ISODATA Algorithm
Fuzzy C-Means
Cluster Analysis
Post-classification Processing
Supervised Classification
Training Data Requirements
Training Site Selection
Feature Space Analysis
Classification Algorithms
Minimum Distance Classifier
Parallelepiped Classifier
Maximum Likelihood Classifier
Mahalanobis Distance
Support Vector Machines
Random Forest
Neural Networks
Decision Trees
Hybrid Classification Approaches
Object-Based Image Analysis
Image Segmentation
Segmentation Algorithms
Scale Parameters
Homogeneity Criteria
Object Feature Extraction
Spectral Features
Spatial Features
Textural Features
Contextual Features
Rule-Based Classification
Hierarchical Classification
Classification Accuracy Assessment
Reference Data Collection
Sampling Design
Error Matrix Construction
Accuracy Metrics
Overall Accuracy
Producer's Accuracy
User's Accuracy
Kappa Coefficient
F1 Score
Statistical Significance Testing
Error Sources and Mitigation
Previous
10. Image Transformations
Go to top
Next
12. Change Detection Analysis