Comparison of Gray-Level Reduction and Different Texture Spectrum Encoding Methods for Land-Use Classification Using a Panchromatic Ikonos Image

Abstract

In this paper, we evaluate the potential of a frequencybased contextual classifier (FBC) for land-use classification with a panchromatic Ikonos image. To capture the spatial arrangement of image gray-level values and use such information in image classification, we applied texture spectrum (TS) directly in the FBC. The effects of several data preprocessing and reduction methods on the performance of the FBC are also evaluated. The methods include four gray-level reduction (GLR) techniques and several modifications to the TS technique. The purpose of data reduction is to improve the classification efficiency of the FBC. The GLR schemes were min-max linear compression (LC), gray level binning (BN), histogram equalization (HE), and piece-wise nonlinear compression (PC). Instead of using the texture measures derived from the texture spectrum, we directly applied texture spectra of various sizes in the classification. We modified the encoding algorithm in the TS and were able to reduce the number of texture units from its original 6561 to 256, 81, and 16, leading to as much as a 410 times computation efficiency. The original image and GLR images were subsequently classified with the FBC. We compared the classification accuracies and found that the GLR methods resulted in accuracies similar to that of the original image (within 0.03 kappa value). There was little difference in classification accuracy (within 0.03 kappa value) among the three modified TS methods, which were all outperformed by the original TS method. All TS methods performed considerably better than the use of the original image and the GLR methods.