Fce 552 part6-4

FCE 552: Engineering Survey IV Dept. of Geospatial & Space Technology

Digital Image Processing

1. Procedures of Image Processing

2. Image Processing Systems

3. Digital Image Processing – Correction

4. Digital Image Processing – Conversion

5. Digital Image Processing – Classification

B. Sc. (Civil Engineering) University of Nairobi


Procedures of Image Processing

Engineering Surveying IV University of Nairobi


Data Flow in Remote Sensing



Data Processing in Remote Sensing



Image Processing Systems



Image Processing Systems with Personal Computer



Image Processing Systems with Network System



Digital Image Processing -
Correction



Radiometric Distortion

 Due to variations in scene illumination and viewing
geometry, atmospheric conditions, and sensor
noise and response will vary depending on the
specific sensor and platform used to acquire the
data and the conditions during data acquisition;
 It may be desirable to convert and/or calibrate
the data to known (absolute) radiation or
reflectance units to facilitate comparison between
data.



Elements of Radiometric Correction



Atmospheric Effects



Atmospheric Correction



Geometric Distortions (1)
Due to several factors, including: the
perspective of the sensor optics; the motion of
the scanning system; the motion of the
platform; the platform altitude, attitude, and
velocity; the terrain relief; and, the curvature
and rotation of the Earth.
To correct for these errors geometric
registration of the imagery to a known ground
coordinate system must be performed i.e.,
georeferencing.


Geometric Distortions (2)



Flow of Geometric Correction



Relationship between Image and
Ground Control Systems



Example of Geometric Correction



Conversion



Types of Image Conversion



Image Enhancement – Grey Scale Conversion



Image Enhancement – Histogram Equalization



Image Enhancement - Color Composition



Feature Extraction - Spatial Filtering



Normalized Difference Vegetation Index



Principal Component Analysis
The objective is to
reduce the dimensionality
in the bands thus
maximizing the amount of
information from the
original data into the
least number of new
components.


Principal Component Analysis



Classification



Definition of Classification



Concept of Classification



Types of Classifications

Supervised classification where the operator defines
the clusters during the training process using training
data obtained from ground truth.
Unsupervised classification involves a clustering
algorithm automatically finding and defining a number
of clusters in the feature space using only image
characteristics or features.



Features for Classification

Multi-spectral features

Multi-temporal features

Texture

Height information (DTM/DEM)

Indices (e.g., NDVI)



Procedures of Classification



Comparison between different classifiers



Advanced Classifications

Fuzzy Classification

Contextual Classification

Artificial Neural Network Classification

Cognition-based Classification



Recognition of Man-made Objects



Example of Road Recognition



Example of Semi-automated Recognition of Houses



Remote Sensing Data Selection Criteria
 Depending on the application, the information of classes
of interest need to be defined and their spatio-temporal
characteristics assessed. On the basis of these, the
most appropriate data can be selected and the criteria
include:-
Sensor type (resolution, level of detail, etc).
Relevant wavelength bands.
Date of acquisition (important for phenomena that are time
dependent, also considerations in regard to cloud cover and
amount of illumination should be taken into account.
Budgetary Criteria.


Fce 552 part6-4

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