An Object-Oriented Classification Method on High
Resolution Satellite Data
2. OBJECT ORIENTED METHOD
In general, the object-oriented classification process can be divided into the two main workflow
steps: multiresolution segmentation and fuzzy classification of the resulting image objects.
Segmentation means the groups of neighbouring pixels into regions or segments based on the
similar criteria such as scale,color and form. Multiresolution segmentation is the first and
important procedure in the eCognition software.It allows the largely knowledge-free extraction
of homogeneous image objects in any chosen resolution,especially taking into consideration
local contrasts. The result of segments act as image objects which can be classifyied in next
step.
eCognition offers two different classifiers: nearest neighbour and membership functions. Both
act as class descriptors. The nearest neighbor classificatioon is similar to the supervised
classification in common image analysis software. It also needs to choose train areas which are
typical representatives of a class. In eCogniton the train areas are named as samples. When user
has no knowledge to describe feature spaces, the nearest neighbor is the best choice. The
membership function method are based on fuzzy logic of segment features to classify image
objects. Fuzzy logic is a mathematical approach to quantify uncertain statements. Membership
functions allow the formulation of knowledge and concepts. They are easy to adapt. Therefore,
if a class can be separated from other classes by a few features or even only one feature, the use
of membership functions is recommended. If you intend to use several object features for a
class decription, the use of nearest neighbor as a classifier is advisable, because the overlaps in
a multidimensional feature space can be handled much better by nearest neighbor than by
membership functions.
3. STUDY AREA AND PRE-PROCESSING OF DATA
The study site is the PuDong New district of ShangHai in China. The test data include
QuickBird panchromatic and multispectral data(see Figure1).
Figure 1. Quickbird panchromatic (left) and multispectral (right)
First of all, the panchromatic image was geo-referenced and then the other images were
geo-referenced by using “image to image” technique. In order to benefit from high spatial
resolution simultaneously with spectral information, Principal component transformation was
applied to resolution merge (Marangoz, 2004). The first principal component of the four
spectral Quickbird channels with 2.4 meter resolution was substituted by the 0.7 meter
resolution Quickbird panchromatic channels. The results of combination then was
re-transformed applying an inverse principal component transformation (see Figure2).
Figure 2. principal component transformation image
