presentation object based

8/2/2019 Presentation Object Based

1/14

Object-based classification of remote sensing datafor change detection

Objective:

A change detection approach based on an object-basedclassification of remote sensing data

This paper shows why object-based classification is better

then pixel based classification in change detection


2/14

pixel based classification


3/14

Problem in pixel based classification

In second step All objects are classified into theclasses

fully verified

partly verified

not found

by using thresholds that can be defined

interactively by the user.

thresholds is data-dependent


4/14

Continue ..

The problem of using thresholds is that they are data-dependent. For example, the percentage of vegetationpixels varies significantly between data that are capturedin summer or in winter.

Other influencing factors are light and weather conditions,soil type, or daytime.

Therefore, we cannot use the same thresholds fordifferent datasets

In order to avoid the problem of defining data-dependentthresholds, we introduce an object-based supervisedclassification approach.


5/14

Object-based classification

We will combine all pixels of each object and classify themtogether.

Training area are derived from database

For the classification of groups of pixels, we have to define newmeasures that can be very simple (e.g., the mean grey value ofall pixels of an object in a specific channel) but also verycomplex, like measures that describe the form of an object.

problematic part of matching is now replaced by a single

comparison of the classification result with the GIS databasewithout using any thresholds.


6/14


7/14

Continue

Divide all object classes into the five land-use classes: water,forest,

settlement, greenland, and roads. The land-use class roads is onlyused in the first step in the process for the pixel basedclassification. Because of the linear shape, roads consist of manymixed pixels in a resolution of 2 m and have to be checked with othertechniques.

In order to analyse the spectral behaviour of objects, we calculate themean grey value of each channel for all GIS objects.

The result of the pixel grouping is like a smoothing of the data


8/14


9/14

Before and After smoothing ,distribution ofobjects

Different land-use classes cannot be distinguished only by their spectral behaviourbut also by their different textures.


10/14

texture

we use a texture operator based on a co-occurrence matrix thatmeasures the contrast in a 5x5 pixel window.(GLCM)

In the case of object-based image analysis, the standard deviation ofreflectance values across all the pixels in the object provides a goodmeasure of texture.


11/14


12/14

variance

The best discrimination between land-use classes using the variance can beseen in the blue band.

In the NIR band, all land-use classes have a similar distribution, which makes

discrimination in this band impossible.


13/14

Using Pixel based classification

we can use the result of a pixel-basedclassification and count the percentage ofpixels that are classified to a specific land-useclass.

An visualisation of the feature space of theobject-based classification can be made withthe combination of three object-based

evaluations of the pixel-based classification.


14/14

presentation object based

Documents