Abstract:
Remote sensing offers efficient and reliable means to recognize
the pattern of the real world and to provide source data for geographic
information system. Supervised learning is traditionally used to classify
remotely sensed imagery data in order to develop land-use mapping. The
classifiers generate inconsequent classes since the complexity of the real
ground features and the parametric variability of the decision rules. This
study is for a new approach of remotely sensed image segmentation for land use mapping of tea plantation. This paper discusses the used supervised
classification approaches in the study, methodology, experiments, results
and the future work of hypothesis testing for the conceptual methodology.
To understand the existing tools several experiments are done through
unsupervised and supervised image classifiers of ERDAS Imagine and RSI
ENVI for a one sample image. Quick Bird-2008 Satellite imagery of Ganga
Ihala Korale division in Kandy district, Sri Lanka is used in a subset of 5.73ha
area. Although each approach generates different outputs the expected
output values are not performed since inconsequent and compound classes.
In contrast Maximum Likelihood classifier shows the highest accuracy for
confusion matrix, Mahalanobis Distance classifier reached the best accuracy
of the manual interpretation and ground truth process. The ongoing research
is focused to modify and implement a threshold scheme for the supervised
learning algorithm with Mahalanobis Distance classifier. The hypothesis is
to test the capability of the classifier to apply spatial information as a
threshold scheme. The algorithm is going to be evaluated using Octave. If the
null hypothesis is rejected, the pixels will be classified by computing
discriminant function for only the signatures corresponding to the spatial
boundaries of land-use types. That means GIS data can be used to increase
the complexity and reliability of hyperplane.
