A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data

Abstract

A large number of spatial feature extraction methods were developed during the past 20 years. The effectiveness of each method has been assessed in different studies using different data. However, there have been few application-oriented studies made to evaluate the relative powers of these methods in a particular environment. In this study, three spatial feature extraction methods have been compared in the land-use classification of the SPOT HRV multispectral data at the rural-urban fringe of Metropolitan Toronto. The first two methods are the well-known gray level co-occurrence matrix (GLCM) and the simple statistical transformation (SST). The third method is the texture spectrum (TS), which was developed recently. Twenty-seven spatial features were derived from the SPOT HRV Band 3 image using these methods. Each of these features or a combination of two of these features were used in combination with the three spectral images in the classification of 10 land-use classes. Results indicated that some spatial features derived using the GLCM and the SST methods can largely improve the classification accuracies obtained by the use of the spectral images only. In addition, average transformed divergence was found to be ineffective in selecting optimal spatial features for land-use classification.