Comparison of non-linear mixture models: sub-pixel classification

Abstract

Sub-pixel level classification is essential for the successful description of many land cover patterns with spatial resolution of less than ~1 km and has been widely used in global or continental scale land cover mapping with remote sensing data. This paper presents a general comparison of four non-linear models for sub-pixel classification: ARTMAP, ART-MMAP, Regression Tree (RT) and Multilayer Perceptron (MLP) with Back-Propagation (BP) algorithm. The comparison is based on four factors: accuracy, model complexity, interpolation ability and error distribution. Two data sets, one simulated and one real world MODIS satellite image, were used to demonstrate the characteristics of each model. Experimental results show the superior performance of MLP with the simulated data set and better performance of ART-MMAP with the MODIS data set.