A comparison of reduced texture spectrum approach with gray level reduction schemes for land-use classification with sampled IKONOS imagery

Abstract

© 2001 IEEE. With the development of high spatial resolution satellites, such as IKONOS, we are able to make more use of spatial features to improve classification accuracy, especially for land-use mapping. In this paper, we compared the texture spectrum (TS) approach with 4 different schemes of gray level reduction, which are min-max linear compression (LC), gray level binning (BN), histogram equalization (HE) and piece-wise nonlinear compression (PC), on the panchromatic band of the sampled IKONOS CARTERRA imagery in terms of overall accuracy and kappa coefficient. The classification algorithm is on the basis of a frequency-based contextual approach that utilizes neighboring information within a particular window. The ranking of 5 different approaches is TS, PC, HE, BN and LC. TS achieves an overall classification accuracy of 72% at a window size of 53 while PC reaches an overall accuracy of 68% at a window size of 35 and LC reaches its highest accuracy of 65% at a window size of 29. TS approach needs a relatively larger window size to achieve higher classification accuracy while the other 4 gray level reduction approaches reach their higher accuracy at smaller window sizes. Within 9 land-use classes, a relatively large window size is more applicable to classify heterogeneous land-use types or areas having first order trend while smaller window sizes work better for more homogeneous land-use types or area with second order effect. The TS approach was found to be more capable of capturing first order trends while the other 4 approaches were more sensitive to local gray level variations thus better capturing the second order effect.