Remote Sensing Mathematics

Stop treating remote sensing algorithms like black boxes. Understand the mathematics that makes them work.

Remote Sensing Mathematics gives you a practical mathematical foundation for interpreting, selecting, troubleshooting, and applying the transformations, filters, and classification algorithms used throughout modern geospatial analysis.
Instead of separating theory from real-world remote sensing, this book connects equations directly to satellite imagery, spectral data, image correction, feature extraction, and classification.
Inside, you’ll learn how to:

• Build confidence with linear algebra, matrices, probability, statistics, covariance, and signal theory as they apply to imagery
• Understand geometric correction, coordinate transformations, radiometric calibration, atmospheric effects, and resampling mathematically
• Work through convolution, spatial filtering, Fourier analysis, wavelets, PCA, spectral transformations, and dimensionality reduction
• Understand statistical classifiers, K-means, support vector machines, random forests, neural networks, and other machine-learning methods
• Evaluate confusion matrices, classification accuracy, uncertainty, cross-validation, and classifier performance
• Explore change detection, CNNs, semantic segmentation, transfer learning, transformers, and other emerging computational methods
• Quickly revisit notation, formulas, terminology, and computational tools through dedicated reference appendices and a glossary

Structured across fifteen chapters, this vendor-neutral reference is designed for GIS professionals, remote sensing analysts, engineers, graduate students, researchers, and data scientists who want to move beyond simply running an algorithm toward understanding why it produces the result it does.

If you want to strengthen the mathematical reasoning behind your remote sensing work, add Remote Sensing Mathematics to your professional library today.