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 extract the features from
remotely sensed imagery data in order to develop land-use mapping. The
classifiers generate irrelevant classes since the complexity of the real ground
features and the parametric variability of the decision rules. To evade from
this issue, this study is for a new approach of remotely sensed image classification.
Spatial data which earned from priori knowledge can bound the signatures
respective to the location of the pixels of input image. For the correlated
case study for land-use mapping of tea plantation, several experiments are done
through unsupervised and supervised image classifiers of ERDAS Imagine and ENVI
image processing software applications 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 in confusion matrix, Mahalanobis Distance classifier
reached the best accuracy of the manual interpretation and ground truth
process. The research is focused to modify and implement a threshold scheme for
the supervised learning algorithm with mahalanobis distance classifier. The hypothesis is to test that could the system consider
only the signatures corresponding to the spatial boundaries and analyzing the
probability of image pixels to be assigned to the classes. If the null
hypothesis is rejected, pixels will be classified as the appropriate classes in
the GIS layer which included spatial boundaries of land-use types. That means GIS data can be used to increase
the complexity and reliability of hyperplane. The purpose of this paper is to
discuss the used supervised classification approaches in the study,
methodology, results and the future work of hypothesis testing. (Last colored
lines will be amended as the final output)
Keywords — Image
Segmentation, Pattern Recognition, Remote Sensing, Supervised Classification.
