Hierarchical maximum-likelihood classification for improved accuracies

Abstract

Among the supervised parametric classification methods, the maximum-likelihood (MLH) classifier has become popular and widespread in remote sensing. Reliable prior probabilities are not always freely available, and it is a common practice to perform the MLH classification with equal prior probabilities. When equal prior probabilities are used, the advantages in MLH classification may not be attained. This study has explored a hierarchical pixel classification (HPC) method to estimate prior probabilities for the spectral classes from the Landsat thematic mapper (TM) data and spectral signatures. The TM pixels were visualized in multidimensional feature space relative to the spectral class probability surfaces. The pixels that fell within more than one probability region or outside all probability regions were categorized as the pixels likely to misclassify. Prior probabilities were estimated from the pixels that fell within spectral class probability regions. The pixels most likely to be correctly classified do not need extra information and were classified according to the probability region in which they fell. The pixels likely to be misclassified need additional information and were classified by MLH classification with the estimated prior probabilities. The classified image resulting from the HPC showed increased accuracy over three classification methods. Visualization of pixels in multidimensional feature space, relative to the spectral class probability reforms, overcome the practical difficulty in estimating prior probabilities while utilizing the available information.