Remote Sensing Image Analysis
Satellite and airborne image processing — classification, spectral indices, change detection, and atmospheric correction for Earth observation
Resolution
(Sentinel-2)
Cycle
Archive
Overview
Remote sensing image analysis transforms raw satellite radiance data into meaningful geospatial information about Earth's surface. The workflow encompasses radiometric calibration, atmospheric correction (converting top-of-atmosphere to surface reflectance), spectral index computation (NDVI, NDWI, MNDWI), image classification (supervised/unsupervised), and change detection. For coastal and ocean applications, these techniques map shoreline change, mangrove extent, water turbidity, coral reef health, aquaculture expansion, and land use/cover dynamics. Modern approaches increasingly integrate deep learning (U-Net, DeepLab) and cloud platforms (Google Earth Engine).
Key Concepts
Atmospheric Correction
Converting TOA radiance to surface reflectance by removing atmospheric scattering/absorption effects. Methods range from dark object subtraction (DOS) to radiative transfer models (6S, MODTRAN). Essential for multi-temporal analysis and accurate spectral indices.
Spectral Indices
Band ratio indices enhance specific features: NDVI = (NIR-Red)/(NIR+Red) for vegetation; NDWI = (Green-NIR)/(Green+NIR) for water; MNDWI = (Green-SWIR)/(Green+SWIR) for urban-water discrimination; FAI for floating algae.
Image Classification
Supervised (Random Forest, SVM, MLC) and unsupervised (K-means, ISODATA) methods assign land cover classes. Object-based image analysis (OBIA) segments pixels into objects before classification. Accuracy assessed via confusion matrix, kappa, F1-score.
Change Detection
Post-classification comparison, image differencing, PCA-based change, and spectral change vectors detect LULC transitions. Multi-temporal composites (BFAST, LandTrendr) detect gradual and abrupt changes in long time series.
Deep Learning for RS
CNNs (U-Net, ResNet, DeepLabV3+) achieve state-of-the-art classification and segmentation. Transfer learning from ImageNet to RS imagery. Semantic segmentation maps every pixel; instance segmentation identifies individual objects (ships, buildings).
Cloud Computing (GEE)
Google Earth Engine provides petabyte-scale access to satellite archives with server-side processing. Enables continental and global analyses without downloading data. Python and JavaScript APIs support reproducible workflows.
Common Sensors
| Sensor | Resolution | Bands | Revisit | Best For |
|---|---|---|---|---|
| Landsat 8/9 (OLI) | 30m (15m pan) | 11 | 8 days (combined) | LULC, long-term change |
| Sentinel-2 (MSI) | 10-60m | 13 | 5 days | Vegetation, water, urban |
| MODIS (Terra/Aqua) | 250-1000m | 36 | Daily | Ocean color, fire, global |
| Sentinel-1 (SAR) | 5-20m | C-band | 6 days | Flood mapping, all-weather |
| Planet (Dove) | 3-5m | 4-8 | Daily | High-frequency monitoring |
| WorldView-3 | 0.31m (pan) | 29 | 1 day | Detailed mapping |
Interactive Visualizations
Spectral Signatures of Common Land Cover Types
Classification Accuracy — Confusion Matrix Heatmap
NDVI Time Series — Mangrove vs. Agriculture vs. Urban
Key References
- Lillesand, T.M., Kiefer, R.W. & Chipman, J.W. (2015). Remote Sensing and Image Interpretation, 7th Ed. Wiley.
- Gorelick, N. et al. (2017). Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
- Zhu, Z. & Woodcock, C.E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171.
- Ma, L. et al. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS J. Photogrammetry and Remote Sensing, 152, 166–177.
- McFeeters, S.K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sensing, 17(7), 1425–1432.