Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Methods for improving supervised and unsupervised classification of remotely sensed data were developed in this study. Supervised classification of remotely sensed data requires systematic collection of training samples for classes of interest. Image visual interpretation is important in training...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2005
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/6062/1/FK_2005_49.pdf http://psasir.upm.edu.my/id/eprint/6062/ |
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| Institution: | Universiti Putra Malaysia |
| Language: | English English |
| Summary: | Methods for improving supervised and unsupervised classification of remotely sensed data
were developed in this study. Supervised classification of remotely sensed data requires
systematic collection of training samples for classes of interest. Image visual interpretation
is important in training samples collection because it incorporates association information
of surrounding pixels, such as texture and context, hence making the training samples
collection process more easy and accurate. Once training samples for each class are
collected, the training statistics for each class and band are extracted to select those bands,
which are most effective in discriminating each class of information from all others for
classification. In remote sensing application, deciding the best band combination for
image visualization and classification is relatively difficult and time consuming. In
addition, the best band selected for image classification is not necessarily the best for
classification.A Best Band Selection Index (BBSI) algorithm was developed which is capable of
selecting the best band combination for image visualization and supervised classification.
This BBSI is calculated by two components, one based on class mean (or cluster mean)
difference and the other based on correlation coefficients. Using Landsat Thematic
Mapper (TM) and ModisIAster Airborne Simulator (hMSTER) images as the test
datasets, the BBSI algorithm was compared to the Optimum Index Factor (OIF) algorithm
in selection of the best three-band combination for image visualization. The comparison
results between BBSI and OIF indicated that, both algorithms correctly predicted the best
three-band combination that provided useful information for image visualization in the
Landsat TM dataset. However, both algorithms tested on MASTER dataset produced
different results. The image quality of band combination selected by BBSI was smoother
and better than OIF.
The BBSI was also compared to the Jefieys-Matusita distance (JM-distance) algorithm in
selection of the best four-band combination for supervised classification of Landsat TM
and MASTER datasets. The comparison results between BBSI and JM-distance showed
that, both algorithms accurately selected the best four-band combination that yielded the
highest overall accuracy classification map with value of 91% in the Landsat TM dataset.
Meanwhile, the comparison results in the MASTER dataset showed that, the overall
accuracy classification map for band combination selected by BBSI with value of 89.7%
was slightly higher than band combination selected by JM-distance with value of 89.2%.Umpervised classification of remotely sensed data consists of cluster generation and
cluster labelling steps. A method was developed to improve the cluster generation and
clusters labelling processes in unsupervised classification of the Landsat TM and
MASTER datasets. In cluster generating process, the developed BBSI algorithm was used
to select the best band combination for generating cluster by using Iterative self-
Organizing Data Analysis (ISODATA) technique. The cluster generation results showed
that, the BBSI accurately selected the best four-band combination generating very low
mixed classes of clusters.
In cluster labelling process, a cluster labelling algorithm based on calculation of
minimum-distance (MD) between cluster mean and class mean was developed to label the
clusters. This algorithm was compared to co-spectral plot method for labelling clusters the
clusters generated in Landsat TM dataset. The comparison results show that, the clusters
labelled by the cluster labelling algorithm were the same as using co-spectral plot. The
cluster labelling algorithm was also compared to maximum-likelihood supervised
classifier in the production of classification map for MASTER dataset. The comparison
showed that, the accuracy of the unsupervised classification map with value of 88.4% that
was generated by using the cluster labelling algorithm was slightly more than the
maximum-likelihood supervised classification map with value of 87.5%. The advantage of
the cluster labelling algorithm compared to co-spectral plot and maximum-likelihood
classifier was the algorithm provided a rapid production of high accuracy classification
map. |
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