Comparison of algorithms for classifying Swedish landcover using Landsat TM and ERS-1 SAR data

Sixteen landcover classes in a representative Swedish environment were analyzed and classified using one Landsat TM scene and seven ERS-1 SARPRI images acquired during 1993. Spectral and backscattering signature separabilities are analyzed using the Jeffries-Matusita distance measure to determine which combinations of channels/images contained the most information. Maximum likelihood, sequential maximum a posteriori (SMAP, a Bayesian image segmentation algorithm), and back propagation neural network classification algorithms were applied and their performances evaluated. Results of the separability analyses indicated that the multitemporal SAR data contained more separable landcover information than did the multispectral TM data; the highest separabilities were achieved when the TM and SAR data were combined. Classification accuracy evaluation results indicate that the SMAP algorithm out-performed the maximum likelihood algorithm which, in turn, outperformed the neural network algorithm. The best KAPPA values, using combined data, were 0.495 for SMAP, 0.0445 for maximum likelihood, and 0.432 for neural network. Corresponding overall accuracy values were 57.1%, 52.4%, and 51.2%, respectively. A comparison between lumped crop area statistics with areal sums calculated from the classified satellite data gave the highest correspondence where the SMAP algorithm was used, followed by the maximum likelihood and neural network algorithms. Based on our application, we can therefore confirm the value of a multisource optical/SAR approach for analyzing landcover and the improvements to classification achieved using the SMAP algorithm. (C)Elsevier Science Inc., 2000.