Comparison On Urban Classifications Using Landsat-Tm And Linear Spectral Mixture Analysis Extracted Images: Nakhon Ratchasima Municipal Area, Thailand

The objective of this research was to compare accuracies of urban land-use classifications of Nakhon Ratchasima municipality and the surrounding area using different types of images and classification methods. Fraction images of green vegetation (V), impervious surface (I), soil (S), and shade (Sh) were generated using Linear Spectral Mixture Analysis (LSMA) with input of their spectral signatures extracted from a scatter-plot of Thematic Mapper (TM) images transformation using Principle Component Analysis (PCA). This resulted in 2 sets of fraction images i.e. V-I-S and V-S-Sh. These 2 sets of fraction images were classified by Maximum Likelihood Classification (MLC) and Endmember Classification (EMC) methods while the original TM images were classified by MLC. Accuracies of 5 resulting urban land-use maps of the study area were assessed by means of error matrix using checking data from field investigation and large-scale color air photos. The assessment revealed that all maps derived from fraction images showed a higher overall accuracy and Kappa statistic than the ones from the original TM images. MLC of the set of V-I-S fraction images provided the highest overall accuracy (72.21%) and MLC of the original TM images provided the lowest overall accuracy (66.93%). Accuracies of land-use classes from the different methods and sets of images based on producer's and user's accuracies were reported and discussed.