Fatwa Ramdani

Geoenvironment, Earth Science, Grad. School of Science

Remote sensing e-course

Semi-Automatic Classification Algorithm: The differences between Minimum Distance, Maximum

Likelihood, and Spectral Angle Mapper

Outline • This course will focus in Semi-Automatic Classification

Algorithm: The differences between Minimum Distance, Maximum Likelihood, and Spectral Angle Mapper based on remotely-sensed data; Landsat 8 OLI. The methods how to analyze and exploit the Landsat 8 OLI information for Land Use mapping will be illustrated in QGIS open source software.

• In final section will be follow with the exercise and questions to allow student expand their understanding.

Course Goal and Objectives

• Understand the concept of Semi-Automatic Classification Algorithm

• Understand algorithm in QGIS open source software

• Understand the differences between Minimum Distance, Maximum Likelihood, and Spectral Angel Mapper algorithm

Intended Audience • University student with basic level of

knowledge in Remote Sensing studies

• Course Requirements:

– Internet access

– QuantumGIS software (http://www.qgis.org/en/site/forusers/download.html)

– Downloaded data

Semi-Automatic Classification Algorithm

General algorithm of imagery classification

Raw DN

Conversion into TOA using DOS method

TOA

Band set Knowledge of the study area

Producing ROI (sampling of training data)

Running Semi-Automatic Classification

Land cover classification

Accuracy assessment

Statistic calculation

Manual input

Minimum Distance

The minimum distance technique uses the mean vectors of each endmember and calculates the Euclidean distance from each unknown pixel to the mean vector for each class. All pixels are classified to the nearest class unless a standard deviation or distance threshold is specified, in which case some pixels may be unclassified if they do not meet the selected criteria.

Reference

Richards, J.A., 1999, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p. 240.

Maximum Likelihood

Maximum likelihood classification assumes that the statistics for each class in each band are normally distributed and calculates the probability that a given pixel belongs to a specific class. Unless you select a probability threshold, all pixels are classified. Each pixel is assigned to the class that has the highest probability (that is, the maximum likelihood). If the highest probability is smaller than a threshold you specify, the pixel remains unclassified.

Reference

Richards, J.A., 1999, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p. 240.

Instead based on training class multispectral distance measurements, the maximum likelihood decision rule is based on probability.

The maximum likelihood procedure assumes that each training class in each band are normally distributed (Gaussian).

The probability of a pixel belonging to each of a predefined set of X classes is calculated, and the pixel is then assigned to the class for which the probability is the highest.

Spectral Angle Mapper

Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an n-D angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra and treating them as vectors in a space with dimensionality equal to the number of bands. This technique, when used on calibrated reflectance data, is relatively insensitive to illumination and albedo effects. Endmember spectra used by SAM can come from ASCII files or spectral libraries, or you can extract them directly from an image (as ROI average spectra). SAM compares the angle between the endmember spectrum vector and each pixel vector in n-D space.

Small angles between the two spectrums indicate high similarity and high angles indicate low similarity. This method is not affected by solar illumination factors, because the angle between the two vectors is independent of vectors length.

SAM classification assumes reflectance data. However, if you use radiance data, the error is generally not significant because the origin is still near zero.

Reference

Kruse, F. A., A. B. Lefkoff, J. B. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz, 1993, “The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging spectrometer Data.” Remote Sensing of the Environment, v. 44, p. 145 - 163.

Activities!

• Check your computer spec, if 64bit then install the WinPhython first

• Download QGIS and install the Semi-Automatic Classification Plugin, run your QGIS and click Plugins – Manage and Install Plugins..

• Learn step-by-step the algorithm of Semi-Automatic Classification

• Compare the result between three different method!

Algorithm Raw DN

Conversion into TOA using DOS method

TOA

Band set Knowledge of the study area

Producing ROI (sampling of training data)

Running Semi-Automatic Classification

Land cover classification

Accuracy assessment

Statistic calculation

Manual input

Exercise!

• Explore the DN and TOA values of different land cover!

• Produce the scatter plot and signature plot of land cover and analyse it!

Application

• Mapping tropical forest environment,

• Mapping wetland environment,

• Vegetation extraction in urban environment,

• Agricultural fields,

• Etc.

Result; RGB 543

Classification Result

Quiz?

• Which method is the best one? Why?

• What are the advantages and the disadvantages of the each method?

• What is the difference between Land Use and Land Cover?

Resources

• Kruse, F. A., A. B. Lefkoff, J. B. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz, 1993, “The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging spectrometer Data.” Remote Sensing of the Environment, v. 44, p. 145 - 163.

• Richards, J.A., 1999, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p. 240.

• http://fatwaramdani.wordpress.com/2014/06/26/land-use-classification-using-qgis/

Read more from Luca Congedo, the author of the Semi-Automatic Classification Plugin for QGIS, here

• http://fromgistors.blogspot.pt/2014/06/land-cover-classification-using-SCP-3.html

Thank you!

Questions?