# Geomatics Engineering GATE Exam Syllabus

## Geomatics Engineering Graduate Aptitude Test in Engineering (GATE) Exam Syllabus

### PART A: Common

Engineering Mathematics - Surveying measurements, Accuracy, Precision, Most probable value,
Statistical significant value, Chi square test.

Remote Sensing - Basic concept, Electromagnetic spectrum, Spectral signature, ResolutionsSpectral. Spatial, Temporal and Radiometric, Platforms and Sensors, Remote Sensing Data

Products - PAN, Multispectral, Microwave, Thermal, Hyperspectral, Visual and digital interpretation
methods

GNSS - Principle used, Components of GNSS, Data collection methods, DGPS, Errors in
observations and corrections.

GIS - Introduction, Data Sources, Data Models and Data Structures, Algorithms, DBMS, Creation of Databases (spatial and non-spatial), Spatial analysis - Interpolation, Buffer, Overlay, Terrain
Modeling and Network analysis.

### PART B: Section I

Maps - Importance of maps to engineering projects, Types of maps, Scales and uses, Plotting accuracy, Map sheet numbering, Coordinate systems- Cartesian and geographical, map projections, map datum – MSL, Geoid, spheroid, WGS-84.

Land Surveying - Various Levels, Levelling methods, Compass, Theodolite and Total Station and their uses, Tachometer, Trigonometric levelling, Traversing, Triangulation and Trilateration.

Aerial Photogrammetry - Types of photographs, Flying height and scale, Relief (height) displacement, Stereoscopy, 3-D Model, Height determination using Parallax Bar, Digital Elevation Model (DEM), Slope.

### PART B: Section II

Data Quantization and Processing - Sampling and quantization theory, Principle of Linear System, Convolution, Continuous and Discrete Fourier Transform.

Digital Image Processing - Digital image characteristics: image histogram and scattergram and their significance, Variance-Covariance matrix, Correlation matrix and their significance.

Radiometric and Geometric Corrections – Registration and Resampling techniques. Image Enhancement – Contrast Enhancement: Linear and Non-linear methods; Spatial Enhancement: Noise and Spatial filters

Image Transformation – Principal Component Analysis (PCA), Discriminant Analysis, Color transformations (RGB - IHS, CMYK), Indices (Ratios, NDVI, NDWI). Image Segmentation and Classification – Simple techniques.