The document compares image classification in ENVI and eCognition software. It details the image classification process for each: in ENVI, images are corrected, filtered, and then classified as supervised or unsupervised; in eCognition, images undergo multiresolution segmentation into objects before creating classes and performing nearest neighbor classification. The conclusion states that ENVI classification is more standardized and supervised, while eCognition uses an object-oriented, hierarchical, trial-and-error approach to segmentation and classification.

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Report on comparison of image
classification on ENVI and Ecognition
Prepared By: Sushmita Timilsina
December, 2013
Department Of Civil and Geomatics Engineering (DOCGE)

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Contents
Provided resources .................................................................................................................................3
Image classification in ENVI ....................................................................................................................3
Image classification in ECOGNITION.......................................................................................................5
Final comparisons ...................................................................................................................................8
Conclusion...............................................................................................................................................9

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Provided resources
 ENVI 4.3 software
 Practical handbook
 Ecognition software with user manual
 Image file
Image classification in ENVI
The image provided to us was of a part of Chitwan District. The image was first subjected to
correction, filtering and then classified to a certain group.
At first the correction was applied to the image. Radiometric consistency among collocated
multi-temporal imagery is difficult to maintain, however, due to variations in sensor
characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure
surface change detection. For the correction we apply radiometric correction. Talking about
radiometric correction, there are two types of radiometric correction namely absolute and
relative radiometric correction. Absolute radiometric models use in situ measurements or
reasonable estimation of 64 X. Remote Sensing of Environment 98 (2005) 6379atmospheric
optical depth, solar zenith angle and satellite status to input parameters for calculating the
ground surface Reflectance. The goal of this study is to develop an improved PIF relative
radiometric normalization method for remote change detection, using multi-spectral imagery.
The goal of this study is to develop an improved PIF relative radiometric normalization
method for remote sensing. Since vegetation is one of our interested area for remote sensing
classification. One of the most popular vegetation indices is the normalised difference
vegetation index (NDVI). NDVI is usually expressed in the following form:
NDVI = (NIR - R) / (NIR + R)
ENVI allows us to calculate spectral indices using bands within a single image or using bands
from several images (as is the case for our data) using the band math function.
When the correction was made radiometric we then proceed to filtering of data. We
underwent with both high and low filter. Convolution filter are the filter that uses a moving
window for filtering and high pass filter are the filter that sharpens the appearance of fine
detail in an image. The moving window are in the form of matrix and the size of the matrix is
defined the user himself e.g 5* 5. The mean of all the pixels present in the matrix of the
original image is taken and is given to the new matrix formed.The high pass filter highlights
or sharpens the appearance of the fine detail in image so the finer details are seen through
high pass convolution.

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Low pass filter is designed to emphasis larger, homogeneous areas of similar tone and reduce
smaller detail in image. Being a convolution filter it also uses moving window average. The
moving window are in the form if matrix; size defined by the user e.g 3*3 or 5*5. In this the
standard deviation of the values of the pixels of the matrix of the original image is taken and
given to the matrix of the new filtered image. The low pass convolution filter filters the lesser
detail from the image. Hence the result is a smoothen image.

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When the correction and filtering was done to the image it was then subjected to the
classification. There were two types of classification made one with the corrected image and
one with the original one. We then compare it with the one classified in ENVI and one on the
ecognition.
Image classification in ECOGNITION
Object-oriented image analysis is divided into three steps:
 Multiresolution Segmentation
 Create General Classes
 Classification Rules
During the first step, image segments are defined and calculated. Parameters are defined by
the 2 user for the scale, spectral properties and shape properties. These image segments have
to be calculated on several hierarchical levels in a “trial and error” process to result in final
image segments to represent single objects of interest . The organization of the workflow is as
follows: 1) Input images, 2) Multiresolution segmentation, 3) Image object hierarchy, 4)
Creation of class hierarchy, 5) Classification using Training samples and standard nearest
neighbor, 6) Classification base Segmentation, 7) Repeat steps for best result, and 8) Final
merge classification. eCognition software is loaded with practice tutorials to understand the
basics of the eCognition software and its tools. The first tutorials show how to load and
display raster data, perform image segmentation, create a simple class hierarchy, insert the
nearest neighbor classifier into the class description, classify, and perform classification
quality assessment.
In ecognition we loaded the image given to us and then we did two procedures as follows
 Chessboard
 Multi-spectral
The image was loaded into eCognition and a spatial subset of the south eastern region of the
image was used. This area included a large water bodies, agriculture area, rural regions.Using
the “Layer Mixing Tool” in eCognition, the image parameters were defined by setting the
Equalizing to histogram and Preset to 3-layer mix.

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The second step is the multiresolution segmentation. The segmentation parameters were
defined. Layer weights were set to equal to one, scale equal to 20, shape factor to 0.3, and
color to 0.7, and compactness and smoothness to 0.5. The scale parameter is an abstract value
to determine the maximum possible change of heterogeneity caused by fusing several objects.
Color is the pixel value. Shape includes compactness and smoothness which are two
geometric features that can be used as "evidence." Smoothness describes the similarity
between the image object borders and a perfect square. Compactness describes the
"closeness" of pixels clustered in an object by comparing it to a circle.

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After defining the parameters, eCognition produces a new image with the new grouping of
pixels.
The next step includes creating Class Hierarchy by creating and defining classes. Three
classes were created: Agriculture, Rural, Wate. These 3 classes will be defined in the next
following steps. After creating the four classes, the tutorial defines the type of classifier
(nearest neighbor or membership functions). Nearest neighbor classifier is used by using the
Edit Standard Nearest Neighbor Feature Space Tool. For each class, the Standard Nearest
Neighbor Expression was inserted. In order to define the four classes, Training Sites of
known areas from the image can be created. With the specific class activated, training sites
can be selected by double clicking on the polygon. Image object data for the selected polygon
can be used. Nearest neighbor classification in eCognition is similar to supervised
classifications in common image analysis software. You have to declare training areas, which
are typical representatives of a class. In eCognition such training areas are referred to as
samples or sample objects. Multiple training sites for an each class were created. The
following color scheme for the training sites are:

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Green (Agriculture),
Blue (Water), Red
brown (Rural).
An accuracy assessment of was performed on the classification results. The best
classification result shows statistics of the training sites and classification description. These
statistics will allow you to compare which classes have the best classification based on the
preliminary results. A confusion matrix of the training sites with the classification was
produced. The overall accuracy is 100% because this statistics based on the samples, not the
final classification. A more accurate statistic can be produced by reclassifying the classes and
defining the class hierarchy with known field data. The known field data trainings sites will
reclassify the image and produce better statistics.
Final comparisons between the image classification between ENVI and Ecognition is
shown below:

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Real image classification from ENVI classification in ecognition
Conclusion
The ENVI has two classification procedure namely supervised and unsupervised. The
unsupervised has two way of performing from k-means and ISO data. The supervised
classification is more standard and expertised. It includes:
 Knowledge Engineer: Definition of hypotheses, rules and variables
 Knowledge Classifier: Rule-based classification
 Iterative approach
Classification in ecognition is object orientated classification. Also the segmentation has gone
through for classification. It includes:
 Hierarchical network of segments in different scale levels
 Choice of proper thresholds for homogeneity and heterogeneity criteria
 Trial & error