GATE Geomatics Engineering Syllabus Download As Pdf
PART A: Common
Engineering Mathematics – Surveying measurements, Accuracy, Precision, Most probable value, Errors and their adjustments, Regression analysis, Correlation coefficient, Least square adjustment, 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.
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