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

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.