Goal and
Background
The
goal of this lab exercise was to become introduced with object-based
classification. This classification is a very accurate classification method
which uses both spectral and spatial information to create land use and cover
classifications. Object-based classification creates shapes for objects and
uses these objects are then classified based on their spatial and spectral
features. eCognition Developer64 by Trimble is the software used for this lab.
This software has object based systems that ERDAS Image is lacking.
Methods
The
first part of the lab was to create a new project. The new project is created
by selecting the new project icon. Importing image layers gives the image that
is to be used in this lab. The image is of Chippewa and Eau Claire counties. In
the Create Project dialog is where all the project information is. Here the Use
geocoding box is selected this allows for the image layer information to be
displayed. Using a single value for all layers is chosen. To set the image as
false color open the Edit Image Layer
Mixing. Here layers 4, 3, and 2 are set as RGB in that order. A process
tree is created which allows creation of a parent process. The parent process
is called <0.001s Lab10_Segment. A child is inserted. The child segmentation
parameters need to be edited. Level 1 is the level name, the scale parameter is
kept at 10, 0.2 and 0.4 are for shape and compactness. This has created image
objects that look like so.
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| Objects created from Object-Based Classification |
Each
class name and class color needs to be set. This is done under the
Classification tab and then Class Hierarchy. Right clicking on anywhere in the
Class Hierarchy will show the Insert Class option. The classes created were
forest as dark green, agriculture as pink, urban/built-up as red, water as
blue, and green vegetation/shrub as light green. The type of classifier needs
to be chosen. The classifier for this is Nearest Neighbor. The pop-up screen
that appears after selecting NN as the classifier will determine the values of
the layers. The Mean is selected. This shows that all features of the mean will
help with classification. Next, is to declare samples. This is found under Classification
as Select Samples. In the Samples menu to choose the class simply clicking the
class sufficed. Double-clicking an object will select that object as the
sample. In the Process Tree on the Lab 10 segment select Append New and create
a classification process. In the Insert Child dialog, in Algorithm Parameters,
selecting all the classes makes sure that they will be used in classification.
Executing in the Edit Process dialog for the classification process will
classify the image. Manual editing can be done if the image is not up to
standards. This is found under the Manual Editing toolbar. Creating a polygon
around misclassified pixels and then selecting the class is a way to reclassify.
Results
The Object-Based Classification looks to be more accurate than some of the other advanced classification methods.
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| Object-Based Classification |
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