Comparison of single-stage and multi-stage classification approaches for cover type mapping with TM and SPOT data

This article presents a comparison of the performance of TM and SPOT data for cover type mapping on the Central Sierra of Spain. A novel multi-stage iterative classification, and four single-step classifications are performed for each type of data. The single-stage classifications differ from one another in the band selection process, the use or not of prior probabilities, and/or the supervised or unsupervised nature of the classification. The accuracy of each classification method and data type (TM vs. SPOT) is expressed as an error matrix from which K statistics and their large sample variances are derived. The values of the K statistics are used to compare the performance of the classification methods, two at time, by means of a Z statistic. Results from this research show that the iterative classification approach is superior to any other classification for both types of remotely sensed data. TM data proves superior to SPOT data only when the iterative classification approach is used. Overall comparison of the performance of TM and SPOT data shows that there is not a statistically significant difference between these types of data for large-scale cover type mapping.